Coral Performance Following Reciprocal Transplant
Serena Hackerott and Carly Travers
1/3/2024
Setup
Load Packages
##Install Packages if Needed
if (!require("ggplot2")) install.packages("ggplot2")Loading required package: ggplot2if (!require("cowplot")) install.packages("cowplot")Loading required package: cowplot
Attaching package: ‘cowplot’
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stampif (!require("lubridate")) install.packages("lubridate")
if (!require("corrplot")) install.packages("corrplot")Loading required package: corrplot
corrplot 0.92 loadedif (!require("lme4")) install.packages("lme4")
if (!require("vegan")) install.packages("vegan")Loading required package: vegan
Loading required package: permute
Loading required package: lattice
This is vegan 2.6-4if (!require("DHARMa")) install.packages("DHARMa")Loading required package: DHARMa
This is DHARMa 0.4.6. For overview type '?DHARMa'. For recent changes, type news(package = 'DHARMa')if (!require("effectsize")) install.packages("effectsize")Loading required package: effectsizeif (!require("emmeans")) install.packages("emmeans")Loading required package: emmeansif (!require("lmerTest")) install.packages("lmerTest")
if (!require("tidyr")) install.packages("tidyr")Loading required package: tidyr
Attaching package: ‘tidyr’
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expand, pack, unpackif (!require("dplyr")) install.packages("dplyr")Loading required package: dplyr
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intersect, setdiff, setequal, unionif (!require("plotrix")) install.packages("plotrix")Loading required package: plotrix
Warning: package ‘plotrix’ was built under R version 4.3.2if (!require("Rmisc")) install.packages("Rmisc")Loading required package: Rmisc
Loading required package: plyr
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If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
library(plyr); library(dplyr)
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Attaching package: ‘plyr’
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get_legend##Load Packages
library(ggplot2) #Required for ggplots
library(cowplot) #Required for plotting panel figures
library(lubridate) #Required for date and time formatting
library(corrplot) #Required for correlation plot
library(lme4) #Required for mixed effects modeling
library(vegan) #Required for multivariate analysis PERM
library(DHARMa) #Required to check residuals of mixed effects models
library(effectsize) #Required for eta_squared effect sizes
library(emmeans) #Required for pairwise comparisons
library(lmerTest) #Required for p values with lme4 model summaries
library(tidyr) #Required for pivot_wider function
library(dplyr) #Required for group_by function
library(plotrix) #Required for Standard error function
library(Rmisc) #Required for SummarySE function
library(ggpubr) #Required for stat brackets
Graphing Parameters
#Note: Run "Graphing Parameters" section from 01_ExperimentalSetup.Rmd fileSample Data and Metadata
Load and Organize Data
##Growth
Length<-read.csv("Data/Length.csv", header=TRUE)
Dates<-read.csv("Data/BonaireDates.csv", header=TRUE)
##Set date variables
Dates$Date<-as.Date(Dates$Date, format='%m/%d/%Y')
##Set factor variables
Length$Site<-factor(Length$Site, levels=c("KL", "SS"))
Length$Genotype<-factor(Length$Genotype, levels=c("AC8", "AC10", "AC12"))
Length$Orig<-factor(Length$Orig, levels=c("N", "T"))
Length$Origin<-factor(Length$Origin, levels=c("Native", "Transplant"))
##Add a Sample Set Variable
Length$Set<-paste(Length$Site, Length$Genotype, Length$Orig, sep=".")
Length$Set<-factor(Length$Set)
##Add Site.Orig variable
Length$Site.Orig<-paste(Length$Site, Length$Orig, sep=".")
Length$Site.Orig<-factor(Length$Site.Orig, levels=c("KL.N", "KL.T", "SS.N", "SS.T"))
##Bleaching Metrics
#Note: Physiological metrics calculated in 02_PhysiologyMetrics.R file
Thermal<-read.csv("Outputs/ThermData.csv", header=TRUE)
##Set factor variables
Thermal$TimeP<-factor(Thermal$TimeP, levels=c("IN", "TP1", "TP2", "TP3", "TP4"))
Thermal$Site<-factor(Thermal$Site, levels=c("KL", "SS"))
Thermal$Genotype<-factor(Thermal$Genotype, levels=c("AC8", "AC10", "AC12"))
Thermal$Orig<-factor(Thermal$Orig, levels=c("N", "T"))
Thermal$Origin<-factor(Thermal$Origin, levels=c("Native", "Transplant"))
Growth
Growth Rate TP1 to TP2
Calculate Days
##Subset Dates for TP 1 and TP 2 Growth
Growth.Dates<-subset(Dates, Task=="Growth" & TimeP=="TP1" |
Task=="Growth" & TimeP=="TP2" )
##Convert to Wide format
Growth.Dates<-Growth.Dates %>% pivot_wider(names_from = TimeP, values_from = Date)
##Calculate Number of Days
#TP 1 to TP2
Growth.Dates$TP1.2_days<-time_length(Growth.Dates$TP2-Growth.Dates$TP1, unit="days")
Calculate Total Linear Extension
##Merge Length Data with Growth Dates
#Merges by Site column
#Adds TP1.2_days column
Growth<-merge(Length, Growth.Dates[,c(-2)], all.x=TRUE)
##Calculate Linear Extension (cm)
Growth$Ext_cm<-Growth$TL.TP2_cm-Growth$TL.TP1_cm
##Calculate Growth Rate (cm/day)
Growth$TLE_cm.day<-Growth$Ext_cm/Growth$TP1.2_days
Data QC
##Sample Size per Set
Growth %>%
dplyr::group_by(Set) %>%
dplyr::summarize(SampleSize = length(Set))
##Check for Outliers
ggplot(Growth, aes(x=Set, y=TLE_cm.day)) +
geom_boxplot(alpha=0.5, shape=2, outlier.shape = NA)+
geom_jitter(shape=16, position=position_jitter(0.1))+
theme(axis.text.x = element_text(angle = 90))
Potential outliers have been re-measured and therefore will be retained. n=10 per Site, Genotype, Origin group
Compare Growth Rate
#Check normality
hist(Growth$TLE_cm.day)
shapiro.test(Growth$TLE_cm.day)
Shapiro-Wilk normality test
data: Growth$TLE_cm.day
W = 0.92803, p-value = 7.262e-06#Not normal
hist(log(Growth$TLE_cm.day+1))
shapiro.test(log(Growth$TLE_cm.day+1))
Shapiro-Wilk normality test
data: log(Growth$TLE_cm.day + 1)
W = 0.94335, p-value = 7.248e-05##Still not normal
##Compare generalized linear mixed effects models
TLE.glmr.gaus<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=gaussian(link="identity"))Warning: calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is deprecated; please call lmer() directlyTLE.glmr.gaus.l<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=gaussian(link="log"))
TLE.glmr.gaus.i<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=gaussian(link="inverse"))
TLE.glmr.gam.l<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=Gamma(link="log"))
TLE.glmr.gam.i<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=Gamma(link="inverse"))
TLE.glmr.inv<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=inverse.gaussian(link="1/mu^2"))boundary (singular) fit: see help('isSingular')TLE.glmr.inv.l<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=inverse.gaussian(link="log"))
TLE.glmr.inv.i<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=inverse.gaussian(link="inverse"))
AIC(TLE.glmr.gaus, TLE.glmr.gaus.l, TLE.glmr.gaus.i, TLE.glmr.gam.l, TLE.glmr.gam.i, TLE.glmr.inv, TLE.glmr.inv.l, TLE.glmr.inv.i)Gamma distribution with log-link has the lowest AIC.
##Check residuals
TLE.glmr.gam.l_res <- simulateResiduals(fittedModel = TLE.glmr.gam.l, plot = F)
plot(TLE.glmr.gam.l_res)
Compare residuals across models
##Check residuals
TLE.glmr.gaus.l_res <- simulateResiduals(fittedModel = TLE.glmr.gaus.l, plot = F)
plot(TLE.glmr.gaus.l_res)
Compare with log+1 transformed lmer model
##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
TLE.lme<-lmer(log(TLE_cm.day+1)~Origin*Site+(1|Genotype), data=Growth)
##Check residuals
TLE.lme_res <- simulateResiduals(fittedModel = TLE.lme, plot = F)
plot(TLE.lme_res)
Better residuals with the log+1 transformed lmer model.
##Model results
summary(TLE.lme)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(TLE_cm.day + 1) ~ Origin * Site + (1 | Genotype)
Data: Growth
REML criterion at convergence: -233.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.0026 -0.5991 -0.1118 0.5959 2.4282
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002965 0.05445
Residual 0.006632 0.08144
Number of obs: 120, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.125179 0.034778 2.684719 3.599 0.04400 *
OriginTransplant -0.007538 0.021027 114.000000 -0.359 0.72063
SiteSS 0.062096 0.021027 114.000000 2.953 0.00382 **
OriginTransplant:SiteSS -0.035167 0.029737 114.000000 -1.183 0.23943
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.302
SiteSS -0.302 0.500
OrgnTrn:SSS 0.214 -0.707 -0.707eta_squared(TLE.lme)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.02 | [0.00, 1.00]
Site | 0.07 | [0.01, 1.00]
Origin:Site | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
TLE.lme_KL<-lmer(log(TLE_cm.day+1)~Origin+(1|Genotype), data=Growth[which(Growth$Site=="KL"),])
summary(TLE.lme_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(TLE_cm.day + 1) ~ Origin + (1 | Genotype)
Data: Growth[which(Growth$Site == "KL"), ]
REML criterion at convergence: -115.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.7586 -0.6776 -0.1453 0.5574 2.2922
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001699 0.04122
Residual 0.006724 0.08200
Number of obs: 60, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.125179 0.028116 2.712715 4.452 0.026 *
OriginTransplant -0.007538 0.021173 56.000006 -0.356 0.723
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.377eta_squared(TLE.lme_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 2.26e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
TLE.lme_SS<-lmer(log(TLE_cm.day+1)~Origin+(1|Genotype), data=Growth[which(Growth$Site=="SS"),])
summary(TLE.lme_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(TLE_cm.day + 1) ~ Origin + (1 | Genotype)
Data: Growth[which(Growth$Site == "SS"), ]
REML criterion at convergence: -114.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.05792 -0.57732 -0.09493 0.72022 2.38351
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.004144 0.06437
Residual 0.006600 0.08124
Number of obs: 60, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.18727 0.04002 2.30548 4.680 0.0323 *
OriginTransplant -0.04271 0.02098 56.00000 -2.036 0.0465 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.262eta_squared(TLE.lme_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.07 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP1.2_Growth.sum<-summarySE(Growth, measurevar="TLE_cm.day", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Growth Rate across Treatments
TP1.2_Growth.plot<-ggplot(TP1.2_Growth.sum, aes(x=Site, y=TLE_cm.day, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TLE_cm.day-se, ymax=TLE_cm.day+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y=expression(paste('Growth Rate (cm day'^-1*")")), colour="Origin")+
ylim(0, 0.25)+
annotate("text", x=2, y=0.24, label="*", size=sig.sz, fontface="bold")+
geom_bracket(xmin=1, xmax=2, y.position=0.01, label.size=levels.sz, label="**", inherit.aes = FALSE); TP1.2_Growth.plot
Average Growth Rates by Genotype
##Summary statistics by Genotype and Origin
TP1.2_Growth.sum.SS<-summarySE(subset(Growth, Site=="SS"), measurevar="TLE_cm.day", groupvars=c("Genotype", "Origin"), na.rm=TRUE)
TP1.2_Growth.sum.SS
##Calculate Percent Difference between Native and Transplant by Genotype
TP1.2_Growth.SS<-TP1.2_Growth.sum.SS[which(TP1.2_Growth.sum.SS$Origin=="Native"), c(1,4)]
names(TP1.2_Growth.SS)[2]<-paste(names(TP1.2_Growth.SS)[2], "N", sep="_")
TP1.2_Growth.SS$TLE_cm.day_T<-TP1.2_Growth.sum.SS$TLE_cm.day[which(TP1.2_Growth.sum.SS$Origin=="Transplant")]
TP1.2_Growth.SS$Perc.Inc<-((TP1.2_Growth.SS$TLE_cm.day_N-TP1.2_Growth.SS$TLE_cm.day_T)/TP1.2_Growth.SS$TLE_cm.day_T)*100
TP1.2_Growth.SS
##Mean and Standard Error
mean(TP1.2_Growth.SS$Perc.Inc)[1] 47.19841std.error(TP1.2_Growth.SS$Perc.Inc)[1] 25.89777Thermal Tolerance
Percent Retention
Subset Thermal Assay Data by Treatment
##Control
Therm_C<-subset(Thermal, Treat=="C")
##Heated
Therm_H<-subset(Thermal, Treat=="H")Calculate Percent Retention
Calculating retention as proportion remaining relative to corresponding control levels (0-1) for each site and genotype at each timepoint.
##Calculate averages for Control Treatment for each Site, Genotype, and Timepoint
Therm_C<-na.omit(Therm_C)
names(Therm_C) [1] "ID" "RandN" "TimeP" "Site" "Genotype" "Treat"
[7] "Treatment" "Orig" "Origin" "Set" "Site.Orig" "SA_cm2"
[13] "Chl_ug.cm2" "Sym10.6_cm2" "Fv_Fm" Therm_C.a<-aggregate(Therm_C[,c(13:15)], list(Therm_C$Site, Therm_C$Genotype, Therm_C$TimeP, Therm_C$Origin), mean, na.action = na.omit)
names(Therm_C.a)[1:4]<-c("Site", "Genotype", "TimeP", "Origin")
names(Therm_C.a)[5:7]<-paste(names(Therm_C.a)[5:7], "C", sep="_")
##Merge Control Averages with Heated Samples
#Merges by Site, Genotype, and Timepoint
Therm_H<-merge(Therm_H, Therm_C.a, all.x=TRUE )
##Calculate Proportion Retained for each Bleaching Metric relative to Control
Therm_H$Chl.prop<-round((Therm_H$Chl_ug.cm2/Therm_H$Chl_ug.cm2_C), 4)
Therm_H$Sym.prop<-round((Therm_H$Sym10.6_cm2/Therm_H$Sym10.6_cm2_C), 4)
Therm_H$PAM.prop<-round((Therm_H$Fv_Fm/Therm_H$Fv_Fm_C), 4)
##Set values >1 to 1
Therm_H$Chl.prop[which(Therm_H$Chl.prop>1)]<-1.0000
Therm_H$Sym.prop[which(Therm_H$Sym.prop>1)]<-1.0000
Therm_H$PAM.prop[which(Therm_H$PAM.prop>1)]<-1.0000
Initial Heat Assay
##Subset Initial Timepoint
Thermal_IN<-subset(Thermal, TimeP=="IN")
Thermal_IN$Site.Treat<-paste(Thermal_IN$Site, Thermal_IN$Treat, sep=".")
Thermal_IN$Site.Treat<-factor(Thermal_IN$Site.Treat, levels=c("KL.C", "KL.H", "SS.C", "SS.H"))
Therm_IN<-subset(Therm_H, TimeP=="IN")Heated vs Control
Symbionts
#Check normality
hist(Thermal_IN$Sym10.6_cm2)
shapiro.test(Thermal_IN$Sym10.6_cm2)
Shapiro-Wilk normality test
data: Thermal_IN$Sym10.6_cm2
W = 0.90824, p-value = 0.001334#Not normal
hist(log(Thermal_IN$Sym10.6_cm2+1))
shapiro.test(log(Thermal_IN$Sym10.6_cm2+1))
Shapiro-Wilk normality test
data: log(Thermal_IN$Sym10.6_cm2 + 1)
W = 0.95667, p-value = 0.07936#Normal
##Model
#Function of Site and Treatment, with Genotype as a Random effect
Sym.lme_IN<-lmer(log(Sym10.6_cm2+1)~Site*Treatment+(1|Genotype), data=Thermal_IN)
##Check residuals
Sym.lme_res_IN <- simulateResiduals(fittedModel = Sym.lme_IN, plot = F)
plot(Sym.lme_res_IN)
##Model results
summary(Sym.lme_IN)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym10.6_cm2 + 1) ~ Site * Treatment + (1 | Genotype)
Data: Thermal_IN
REML criterion at convergence: -29.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.79325 -0.66013 0.08255 0.57806 2.12486
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0008222 0.02867
Residual 0.0230525 0.15183
Number of obs: 47, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.38823 0.04685 14.06727 8.286 8.77e-07 ***
SiteSS 0.19508 0.06198 41.00308 3.147 0.00307 **
TreatmentHeat -0.20370 0.06340 41.10985 -3.213 0.00256 **
SiteSS:TreatmentHeat -0.01724 0.08867 41.05799 -0.194 0.84677
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SiteSS TrtmnH
SiteSS -0.661
TreatmentHt -0.647 0.489
StSS:TrtmnH 0.462 -0.699 -0.715eta_squared(Sym.lme_IN)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
----------------------------------------------
Site | 0.30 | [0.12, 1.00]
Treatment | 0.36 | [0.17, 1.00]
Site:Treatment | 9.20e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].emmeans(Sym.lme_IN, pairwise ~ Site | Treatment)$emmeans
Treatment = Control:
Site emmean SE df lower.CL upper.CL
KL 0.388 0.0469 14.1 0.2878 0.489
SS 0.583 0.0469 14.1 0.4829 0.684
Treatment = Heat:
Site emmean SE df lower.CL upper.CL
KL 0.185 0.0488 15.6 0.0808 0.288
SS 0.362 0.0469 14.1 0.2619 0.463
Degrees-of-freedom method: kenward-roger
Results are given on the log(mu + 1) (not the response) scale.
Confidence level used: 0.95
$contrasts
Treatment = Control:
contrast estimate SE df t.ratio p.value
KL - SS -0.195 0.0620 41.0 -3.147 0.0031
Treatment = Heat:
contrast estimate SE df t.ratio p.value
KL - SS -0.178 0.0635 41.1 -2.801 0.0077
Note: contrasts are still on the log(mu + 1) scale
Degrees-of-freedom method: kenward-roger emmeans(Sym.lme_IN, pairwise ~ Treatment | Site)$emmeans
Site = KL:
Treatment emmean SE df lower.CL upper.CL
Control 0.388 0.0469 14.1 0.2878 0.489
Heat 0.185 0.0488 15.6 0.0808 0.288
Site = SS:
Treatment emmean SE df lower.CL upper.CL
Control 0.583 0.0469 14.1 0.4829 0.684
Heat 0.362 0.0469 14.1 0.2619 0.463
Degrees-of-freedom method: kenward-roger
Results are given on the log(mu + 1) (not the response) scale.
Confidence level used: 0.95
$contrasts
Site = KL:
contrast estimate SE df t.ratio p.value
Control - Heat 0.204 0.0635 41.1 3.209 0.0026
Site = SS:
contrast estimate SE df t.ratio p.value
Control - Heat 0.221 0.0620 41.0 3.564 0.0009
Note: contrasts are still on the log(mu + 1) scale
Degrees-of-freedom method: kenward-roger ##Summary statistics by Site
IN_Sym.sum<-summarySE(Thermal_IN, measurevar="Sym10.6_cm2", groupvars=c("Site", "Treatment", "Site.Treat"), na.rm=TRUE)
##Plot Average Symbionts across Treatments
IN_Sym.plot<-ggplot(IN_Sym.sum, aes(x=Site.Treat, y=Sym10.6_cm2, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Sym10.6_cm2-se, ymax=Sym10.6_cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
scale_shape_manual(values=c(1,16))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Treatment", y=expression(paste('Symbiont Density ('*10^6,'cells cm'^-2*")")), colour="Site")+
ylim(0, 1); IN_Sym.plot
Chlorophyll
#Check normality
hist(Thermal_IN$Chl_ug.cm2)
shapiro.test(Thermal_IN$Chl_ug.cm2)
Shapiro-Wilk normality test
data: Thermal_IN$Chl_ug.cm2
W = 0.88059, p-value = 0.0002132#Not normal
hist(log(Thermal_IN$Chl_ug.cm2+1))
shapiro.test(log(Thermal_IN$Chl_ug.cm2+1))
Shapiro-Wilk normality test
data: log(Thermal_IN$Chl_ug.cm2 + 1)
W = 0.92511, p-value = 0.005653#Still not normal but less skewed
##Model
#Function of Site and Treatment, with Genotype as a Random effect
Chl.lme_IN<-lmer(log(Chl_ug.cm2+1)~Site*Treatment+(1|Genotype), data=Thermal_IN)
##Check residuals
Chl.lme_res_IN <- simulateResiduals(fittedModel = Chl.lme_IN, plot = F)
plot(Chl.lme_res_IN)
##Model results
summary(Chl.lme_IN)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl_ug.cm2 + 1) ~ Site * Treatment + (1 | Genotype)
Data: Thermal_IN
REML criterion at convergence: -69.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.50589 -0.51517 0.07352 0.50169 2.35158
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.006137 0.07834
Residual 0.007930 0.08905
Number of obs: 46, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.45648 0.05202 2.95779 8.774 0.00329 **
SiteSS 0.27810 0.03635 39.99255 7.650 2.38e-09 ***
TreatmentHeat -0.37295 0.03721 40.00578 -10.024 1.80e-12 ***
SiteSS:TreatmentHeat -0.16117 0.05265 40.01278 -3.061 0.00393 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SiteSS TrtmnH
SiteSS -0.349
TreatmentHt -0.341 0.489
StSS:TrtmnH 0.241 -0.691 -0.707eta_squared(Chl.lme_IN)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
----------------------------------------------
Site | 0.58 | [0.41, 1.00]
Treatment | 0.88 | [0.82, 1.00]
Site:Treatment | 0.19 | [0.04, 1.00]
- One-sided CIs: upper bound fixed at [1.00].emmeans(Chl.lme_IN, pairwise ~ Site | Treatment)$emmeans
Treatment = Control:
Site emmean SE df lower.CL upper.CL
KL 0.4565 0.0520 2.97 0.2899 0.623
SS 0.7346 0.0520 2.97 0.5680 0.901
Treatment = Heat:
Site emmean SE df lower.CL upper.CL
KL 0.0835 0.0526 3.11 -0.0808 0.248
SS 0.2005 0.0526 3.11 0.0361 0.365
Degrees-of-freedom method: kenward-roger
Results are given on the log(mu + 1) (not the response) scale.
Confidence level used: 0.95
$contrasts
Treatment = Control:
contrast estimate SE df t.ratio p.value
KL - SS -0.278 0.0364 40 -7.650 <.0001
Treatment = Heat:
contrast estimate SE df t.ratio p.value
KL - SS -0.117 0.0381 40 -3.070 0.0038
Note: contrasts are still on the log(mu + 1) scale
Degrees-of-freedom method: kenward-roger emmeans(Chl.lme_IN, pairwise ~ Treatment | Site)$emmeans
Site = KL:
Treatment emmean SE df lower.CL upper.CL
Control 0.4565 0.0520 2.97 0.2899 0.623
Heat 0.0835 0.0526 3.11 -0.0808 0.248
Site = SS:
Treatment emmean SE df lower.CL upper.CL
Control 0.7346 0.0520 2.97 0.5680 0.901
Heat 0.2005 0.0526 3.11 0.0361 0.365
Degrees-of-freedom method: kenward-roger
Results are given on the log(mu + 1) (not the response) scale.
Confidence level used: 0.95
$contrasts
Site = KL:
contrast estimate SE df t.ratio p.value
Control - Heat 0.373 0.0372 40 10.022 <.0001
Site = SS:
contrast estimate SE df t.ratio p.value
Control - Heat 0.534 0.0372 40 14.353 <.0001
Note: contrasts are still on the log(mu + 1) scale
Degrees-of-freedom method: kenward-roger ##Summary statistics by Site
IN_Chl.sum<-summarySE(Thermal_IN, measurevar="Chl_ug.cm2", groupvars=c("Site", "Treatment", "Site.Treat"), na.rm=TRUE)
##Plot Average Chlorophyll across Treatments
IN_Chl.plot<-ggplot(IN_Chl.sum, aes(x=Site.Treat, y=Chl_ug.cm2, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
scale_shape_manual(values=c(1,16))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Treatment", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Site")+
ylim(0, 1.25); IN_Chl.plot
FvFm
#Check normality
hist(Thermal_IN$Fv_Fm)
shapiro.test(Thermal_IN$Fv_Fm)
Shapiro-Wilk normality test
data: Thermal_IN$Fv_Fm
W = 0.87006, p-value = 9.097e-05#Not normal
hist(log(Thermal_IN$Fv_Fm+1))
shapiro.test(log(Thermal_IN$Fv_Fm+1))
Shapiro-Wilk normality test
data: log(Thermal_IN$Fv_Fm + 1)
W = 0.85365, p-value = 3.226e-05#Still not normal but less skewed
##Model
#Function of Site and Treatment, with Genotype as a Random effect
PAM.lme_IN<-lmer(log(Fv_Fm+1)~Site*Treatment+(1|Genotype), data=Thermal_IN)boundary (singular) fit: see help('isSingular')##Check residuals
PAM.lme_res_IN <- simulateResiduals(fittedModel = PAM.lme_IN, plot = F)
plot(PAM.lme_res_IN)
##Model results
summary(PAM.lme_IN)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Fv_Fm + 1) ~ Site * Treatment + (1 | Genotype)
Data: Thermal_IN
REML criterion at convergence: -202.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.7092 -0.4159 0.1391 0.6366 1.5720
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0000000 0.00000
Residual 0.0004174 0.02043
Number of obs: 47, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.480202 0.005897 43.000000 81.425 < 2e-16 ***
SiteSS 0.013981 0.008340 43.000000 1.676 0.101
TreatmentHeat -0.044190 0.008528 43.000000 -5.182 5.57e-06 ***
SiteSS:TreatmentHeat 0.022162 0.011928 43.000000 1.858 0.070 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) SiteSS TrtmnH
SiteSS -0.707
TreatmentHt -0.692 0.489
StSS:TrtmnH 0.494 -0.699 -0.715
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(PAM.lme_IN)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
----------------------------------------------
Site | 0.29 | [0.12, 1.00]
Treatment | 0.42 | [0.23, 1.00]
Site:Treatment | 0.07 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].emmeans(PAM.lme_IN, pairwise ~ Site | Treatment)$emmeans
Treatment = Control:
Site emmean SE df lower.CL upper.CL
KL 0.480 0.00590 21.6 0.468 0.492
SS 0.494 0.00590 21.6 0.482 0.506
Treatment = Heat:
Site emmean SE df lower.CL upper.CL
KL 0.436 0.00618 23.5 0.423 0.449
SS 0.472 0.00590 21.6 0.460 0.484
Degrees-of-freedom method: kenward-roger
Results are given on the log(mu + 1) (not the response) scale.
Confidence level used: 0.95
$contrasts
Treatment = Control:
contrast estimate SE df t.ratio p.value
KL - SS -0.0140 0.00834 41.0 -1.676 0.1013
Treatment = Heat:
contrast estimate SE df t.ratio p.value
KL - SS -0.0361 0.00855 41.2 -4.229 0.0001
Note: contrasts are still on the log(mu + 1) scale
Degrees-of-freedom method: kenward-roger emmeans(PAM.lme_IN, pairwise ~ Treatment | Site)$emmeans
Site = KL:
Treatment emmean SE df lower.CL upper.CL
Control 0.480 0.00590 21.6 0.468 0.492
Heat 0.436 0.00618 23.5 0.423 0.449
Site = SS:
Treatment emmean SE df lower.CL upper.CL
Control 0.494 0.00590 21.6 0.482 0.506
Heat 0.472 0.00590 21.6 0.460 0.484
Degrees-of-freedom method: kenward-roger
Results are given on the log(mu + 1) (not the response) scale.
Confidence level used: 0.95
$contrasts
Site = KL:
contrast estimate SE df t.ratio p.value
Control - Heat 0.0442 0.00855 41.2 5.171 <.0001
Site = SS:
contrast estimate SE df t.ratio p.value
Control - Heat 0.0220 0.00834 41.0 2.641 0.0116
Note: contrasts are still on the log(mu + 1) scale
Degrees-of-freedom method: kenward-roger Not normal, but model residuals are OK.
##Summary statistics by Site
IN_PAM.sum<-summarySE(Thermal_IN, measurevar="Fv_Fm", groupvars=c("Site", "Treatment", "Site.Treat"), na.rm=TRUE)
##Plot Average FvFm across Treatments
IN_PAM.plot<-ggplot(IN_PAM.sum, aes(x=Site.Treat, y=Fv_Fm, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Fv_Fm-se, ymax=Fv_Fm+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
scale_shape_manual(values=c(1,16))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Treatment", y="Photochemical Efficiency (Fv/Fm)", colour="Site")+
ylim(0.5, 0.655); IN_PAM.plot
Retention by Site
Symbionts
#Check normality
hist(Therm_IN$Sym.prop)
shapiro.test(Therm_IN$Sym.prop)
Shapiro-Wilk normality test
data: Therm_IN$Sym.prop
W = 0.93042, p-value = 0.1117#Normal
##Model
#Function of Site, with Genotype as a Random effect
Tol_Sym.lme_IN<-lmer(Sym.prop~Site+(1|Genotype), data=Therm_IN)
##Check residuals
Tol_Sym.lme_res_IN <- simulateResiduals(fittedModel = Tol_Sym.lme_IN, plot = F)
plot(Tol_Sym.lme_res_IN)
##Model results
summary(Tol_Sym.lme_IN)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Sym.prop ~ Site + (1 | Genotype)
Data: Therm_IN
REML criterion at convergence: -2.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.6802 -0.6578 -0.1521 0.6805 1.9852
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.07818 0.2796
Residual 0.03039 0.1743
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.46139 0.16983 2.21175 2.717 0.1014
SiteSS 0.14316 0.07291 19.00328 1.964 0.0644 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
SiteSS -0.225eta_squared(Tol_Sym.lme_IN)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Site | 0.17 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site
IN_TolSym.sum<-summarySE(Therm_IN, measurevar="Sym.prop", groupvars=c("Site"), na.rm=TRUE)
##Plot Average Symbiont Retention across Treatments
IN_TolSym.plot<-ggplot(IN_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site", y="Symbiont Retention", colour="Site")+
ylim(0, 1)+
annotate("text", x=1.5, y=0.75, label="-", size=sig.sz, fontface="bold"); IN_TolSym.plot
Chlorophyll
#Check normality
hist(Therm_IN$Chl.prop)
shapiro.test(Therm_IN$Chl.prop)
Shapiro-Wilk normality test
data: Therm_IN$Chl.prop
W = 0.98432, p-value = 0.9694#Normal
##Model
#Function of Site, with Genotype as a Random effect
Tol_Chl.lme_IN<-lmer(Chl.prop~Site+(1|Genotype), data=Therm_IN)
##Check residuals
Tol_Chl.lme_res_IN <- simulateResiduals(fittedModel = Tol_Chl.lme_IN, plot = F)
plot(Tol_Chl.lme_res_IN)
##Model results
summary(Tol_Chl.lme_IN)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl.prop ~ Site + (1 | Genotype)
Data: Therm_IN
REML criterion at convergence: -47.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.3909 -0.5550 -0.1823 0.5284 1.9947
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.004576 0.06765
Residual 0.003436 0.05861
Number of obs: 22, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.16552 0.04290 2.40867 3.858 0.045 *
SiteSS 0.04306 0.02514 18.06310 1.713 0.104
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
SiteSS -0.293eta_squared(Tol_Chl.lme_IN)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Site | 0.14 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
IN_TolChl.sum<-summarySE(Therm_IN, measurevar="Chl.prop", groupvars=c("Site"), na.rm=TRUE)
##Plot Average Chlorophyll Retention across Treatments
IN_TolChl.plot<-ggplot(IN_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site", y="Chlorophyll Retention", colour="Site")+
ylim(0, 0.35); IN_TolChl.plot
FvFm
#Check normality
hist(Therm_IN$PAM.prop)
shapiro.test(Therm_IN$PAM.prop)
Shapiro-Wilk normality test
data: Therm_IN$PAM.prop
W = 0.8289, p-value = 0.001154#Not normal
hist(log(Therm_IN$PAM.prop+1))
shapiro.test(log(Therm_IN$PAM.prop+1))
Shapiro-Wilk normality test
data: log(Therm_IN$PAM.prop + 1)
W = 0.81061, p-value = 0.0005693##Still not normal
##Model
#Function of Site, with Genotype as a Random effect
Tol_PAM.lme_IN<-lmer(PAM.prop~Site+(1|Genotype), data=Therm_IN)
##Check residuals
Tol_PAM.lme_res_IN <- simulateResiduals(fittedModel = Tol_PAM.lme_IN, plot = F)
plot(Tol_PAM.lme_res_IN)
##Model results
summary(Tol_PAM.lme_IN)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Site + (1 | Genotype)
Data: Therm_IN
REML criterion at convergence: -50.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.7860 -0.2498 0.1225 0.5362 1.4591
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002221 0.04713
Residual 0.003538 0.05948
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.89138 0.03262 2.77551 27.325 0.000185 ***
SiteSS 0.05230 0.02487 19.00103 2.103 0.049053 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
SiteSS -0.399eta_squared(Tol_PAM.lme_IN)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Site | 0.19 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Not normal, but model residuals are OK.
##Summary statistics by Site and Origin
IN_TolPAM.sum<-summarySE(Therm_IN, measurevar="PAM.prop", groupvars=c("Site"), na.rm=TRUE)
##Plot Average FvFm Retention across Treatments
IN_TolPAM.plot<-ggplot(IN_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site", y="Photochemical Efficiency Retention", colour="Site")+
ylim(0.6, 1)+
annotate("text", x=1.5, y=1, label="*", size=sig.sz, fontface="bold"); IN_TolPAM.plot
Tolerance after Transplant
TP1
##Subset Timepoint 1
Therm_TP1<-subset(Therm_H, TimeP=="TP1")Symbionts
#Check normality
hist(Therm_TP1$Sym.prop)
shapiro.test(Therm_TP1$Sym.prop)
Shapiro-Wilk normality test
data: Therm_TP1$Sym.prop
W = 0.91699, p-value = 0.002332#Not normal
hist(log(Therm_TP1$Sym.prop+1))
shapiro.test(log(Therm_TP1$Sym.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP1$Sym.prop + 1)
W = 0.92258, p-value = 0.003671##Still not normal but less skewed
##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Sym.lme_TP1<-lmer(log(Sym.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP1)
##Check residuals
Tol_Sym.lme_res_TP1 <- simulateResiduals(fittedModel = Tol_Sym.lme_TP1, plot = F)
plot(Tol_Sym.lme_res_TP1)
##Model results
summary(Tol_Sym.lme_TP1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin * Site + (1 | Genotype)
Data: Therm_TP1
REML criterion at convergence: -33.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.1715 -0.5714 0.0059 0.7146 1.9799
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.03376 0.1837
Residual 0.01886 0.1373
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.27621 0.11324 2.42412 2.439 0.1128
OriginTransplant 0.11668 0.05606 42.00000 2.081 0.0435 *
SiteSS 0.06309 0.05606 42.00000 1.125 0.2668
OriginTransplant:SiteSS -0.13250 0.07928 42.00000 -1.671 0.1021
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.248
SiteSS -0.248 0.500
OrgnTrn:SSS 0.175 -0.707 -0.707eta_squared(Tol_Sym.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.04 | [0.00, 1.00]
Site | 1.51e-04 | [0.00, 1.00]
Origin:Site | 0.06 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Not normal, but model residuals are OK.
Effect size of Origin for each Site
##KL
Tol_Sym.lme_TP1_KL<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="KL"),])
summary(Tol_Sym.lme_TP1_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP1[which(Therm_TP1$Site == "KL"), ]
REML criterion at convergence: -21.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.22211 -0.64466 0.06469 0.75749 1.28798
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.04259 0.2064
Residual 0.01291 0.1136
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.27621 0.12358 2.14836 2.235 0.1461
OriginTransplant 0.11668 0.04638 20.00000 2.516 0.0205 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.188eta_squared(Tol_Sym.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.24 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_Sym.lme_TP1_SS<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="SS"),])
summary(Tol_Sym.lme_TP1_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP1[which(Therm_TP1$Site == "SS"), ]
REML criterion at convergence: -8.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.4670 -0.5662 0.1529 0.6190 1.4817
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.02355 0.1535
Residual 0.02580 0.1606
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.33930 0.10000 2.50724 3.393 0.0557 .
OriginTransplant -0.01583 0.06558 20.00000 -0.241 0.8118
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.328eta_squared(Tol_Sym.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 2.90e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP1_TolSym.sum<-summarySE(Therm_TP1, measurevar="Sym.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Symbiont Retention across Treatments
TP1_TolSym.plot<-ggplot(TP1_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Symbiont Retention", colour="Origin")+
ylim(0, 1)+
annotate("text", x=1, y=0.75, label="*", size=sig.sz, fontface="bold"); TP1_TolSym.plot
Chlorophyll
#Check normality
hist(Therm_TP1$Chl.prop)
shapiro.test(Therm_TP1$Chl.prop)
Shapiro-Wilk normality test
data: Therm_TP1$Chl.prop
W = 0.94139, p-value = 0.01836#Not normal
hist(log(Therm_TP1$Chl.prop+1))
shapiro.test(log(Therm_TP1$Chl.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP1$Chl.prop + 1)
W = 0.94765, p-value = 0.03227##Still not normal but less skewed
##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Chl.lme_TP1<-lmer(log(Chl.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP1)
##Check residuals
Tol_Chl.lme_res_TP1 <- simulateResiduals(fittedModel = Tol_Chl.lme_TP1, plot = F)
plot(Tol_Chl.lme_res_TP1)
##Model results
summary(Tol_Chl.lme_TP1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin * Site + (1 | Genotype)
Data: Therm_TP1
REML criterion at convergence: -112.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.1439 -0.5458 -0.0382 0.6267 1.8760
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.008382 0.09155
Residual 0.003065 0.05537
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.13931 0.05522 2.27664 2.523 0.113
OriginTransplant 0.01521 0.02260 42.00000 0.673 0.505
SiteSS 0.02628 0.02260 42.00000 1.163 0.251
OriginTransplant:SiteSS -0.02689 0.03197 42.00000 -0.841 0.405
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.205
SiteSS -0.205 0.500
OrgnTrn:SSS 0.145 -0.707 -0.707eta_squared(Tol_Chl.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 2.90e-04 | [0.00, 1.00]
Site | 0.02 | [0.00, 1.00]
Origin:Site | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Tol_Chl.lme_TP1_KL<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="KL"),])
summary(Tol_Chl.lme_TP1_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP1[which(Therm_TP1$Site == "KL"), ]
REML criterion at convergence: -55.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.04878 -0.58286 0.01887 0.57011 1.44796
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.007318 0.08555
Residual 0.002846 0.05334
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.13931 0.05173 2.18924 2.693 0.104
OriginTransplant 0.01521 0.02178 20.00000 0.698 0.493
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.210eta_squared(Tol_Chl.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_Chl.lme_TP1_SS<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="SS"),])
summary(Tol_Chl.lme_TP1_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP1[which(Therm_TP1$Site == "SS"), ]
REML criterion at convergence: -50.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.43149 -0.56754 -0.06902 0.58853 1.85344
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.009135 0.09558
Residual 0.003511 0.05926
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.16560 0.05777 2.18712 2.866 0.0931 .
OriginTransplant -0.01168 0.02419 20.00000 -0.483 0.6344
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.209eta_squared(Tol_Chl.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.01 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP1_TolChl.sum<-summarySE(Therm_TP1, measurevar="Chl.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Chlorophyll Retention across Treatments
TP1_TolChl.plot<-ggplot(TP1_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
ylim(0, 0.35); TP1_TolChl.plot
FvFm
#Check normality
hist(Therm_TP1$PAM.prop)
shapiro.test(Therm_TP1$PAM.prop)
Shapiro-Wilk normality test
data: Therm_TP1$PAM.prop
W = 0.84073, p-value = 1.245e-05#Not normal
hist(log(Therm_TP1$PAM.prop+1))
shapiro.test(log(Therm_TP1$PAM.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP1$PAM.prop + 1)
W = 0.82049, p-value = 3.908e-06##Still not normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_PAM.lme_TP1<-lmer(PAM.prop~Origin*Site+(1|Genotype), data=Therm_TP1)
##Check residuals
Tol_PAM.lme_res_TP1 <- simulateResiduals(fittedModel = Tol_PAM.lme_TP1, plot = F)
plot(Tol_PAM.lme_res_TP1)
##Model results
summary(Tol_PAM.lme_TP1)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin * Site + (1 | Genotype)
Data: Therm_TP1
REML criterion at convergence: -84.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.3058 -0.3498 0.1733 0.6717 1.7818
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002735 0.05230
Residual 0.006214 0.07883
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.86181 0.03781 3.74555 22.794 3.72e-05 ***
OriginTransplant 0.06813 0.03218 42.00000 2.117 0.0402 *
SiteSS -0.01277 0.03218 42.00000 -0.397 0.6936
OriginTransplant:SiteSS -0.07942 0.04551 42.00000 -1.745 0.0883 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.426
SiteSS -0.426 0.500
OrgnTrn:SSS 0.301 -0.707 -0.707eta_squared(Tol_PAM.lme_TP1)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.04 | [0.00, 1.00]
Site | 0.11 | [0.01, 1.00]
Origin:Site | 0.07 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Not normal, but model residuals are OK.
Effect size of Origin for each Site
##KL
Tol_PAM.lme_TP1_KL<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="KL"),])
summary(Tol_PAM.lme_TP1_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin + (1 | Genotype)
Data: Therm_TP1[which(Therm_TP1$Site == "KL"), ]
REML criterion at convergence: -57.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.21445 -0.36299 0.09321 0.61667 1.51097
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0009092 0.03015
Residual 0.0031116 0.05578
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.86181 0.02371 3.34791 36.341 1.82e-05 ***
OriginTransplant 0.06813 0.02277 20.00000 2.992 0.00721 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.480eta_squared(Tol_PAM.lme_TP1_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.31 | [0.06, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_PAM.lme_TP1_SS<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="SS"),])
summary(Tol_PAM.lme_TP1_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin + (1 | Genotype)
Data: Therm_TP1[which(Therm_TP1$Site == "SS"), ]
REML criterion at convergence: -33.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.69510 -0.36658 0.09367 0.51233 1.74473
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.005306 0.07284
Residual 0.008775 0.09367
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.84904 0.05000 2.73586 16.981 0.000746 ***
OriginTransplant -0.01128 0.03824 20.00000 -0.295 0.770994
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.382eta_squared(Tol_PAM.lme_TP1_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 4.33e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP1_TolPAM.sum<-summarySE(Therm_TP1, measurevar="PAM.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average FvFm Retention across Treatments
TP1_TolPAM.plot<-ggplot(TP1_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Photochemical Efficiency Retention", colour="Origin")+
ylim(0.6, 1)+
annotate("text", x=1, y=1, label="**", size=sig.sz, fontface="bold"); TP1_TolPAM.plot
TP2
##Subset Timepoint 2
Therm_TP2<-subset(Therm_H, TimeP=="TP2")Symbionts
#Check normality
hist(Therm_TP2$Sym.prop)
shapiro.test(Therm_TP2$Sym.prop)
Shapiro-Wilk normality test
data: Therm_TP2$Sym.prop
W = 0.89527, p-value = 0.0004439#Not normal
hist(log(Therm_TP2$Sym.prop+1))
shapiro.test(log(Therm_TP2$Sym.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP2$Sym.prop + 1)
W = 0.88978, p-value = 0.000299##Still not normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Sym.lme_TP2<-lmer(Sym.prop~Origin*Site+(1|Genotype), data=Therm_TP2)
##Check residuals
Tol_Sym.lme_res_TP2 <- simulateResiduals(fittedModel = Tol_Sym.lme_TP2, plot = F)
plot(Tol_Sym.lme_res_TP2)
##Model results
summary(Tol_Sym.lme_TP2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Sym.prop ~ Origin * Site + (1 | Genotype)
Data: Therm_TP2
REML criterion at convergence: -3.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.20381 -0.54227 0.01202 0.47665 2.09092
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.04681 0.2164
Residual 0.03767 0.1941
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.805833 0.136905 2.613359 5.886 0.0142 *
OriginTransplant -0.107900 0.079237 42.000000 -1.362 0.1805
SiteSS -0.178425 0.079237 42.000000 -2.252 0.0296 *
OriginTransplant:SiteSS 0.005183 0.112058 42.000000 0.046 0.9633
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.289
SiteSS -0.289 0.500
OrgnTrn:SSS 0.205 -0.707 -0.707eta_squared(Tol_Sym.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.08 | [0.00, 1.00]
Site | 0.19 | [0.04, 1.00]
Origin:Site | 5.09e-05 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Not normal, but model residuals are OK.
Effect size of Origin for each Site
##KL
Tol_Sym.lme_TP2_KL<-lmer(Sym.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="KL"),])
summary(Tol_Sym.lme_TP2_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Sym.prop ~ Origin + (1 | Genotype)
Data: Therm_TP2[which(Therm_TP2$Site == "KL"), ]
REML criterion at convergence: -1.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.0500 -0.8276 0.2039 0.4804 2.0614
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.04096 0.2024
Residual 0.03487 0.1867
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.80583 0.12868 2.40096 6.262 0.0154 *
OriginTransplant -0.10790 0.07623 20.00000 -1.415 0.1723
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.296eta_squared(Tol_Sym.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.09 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_Sym.lme_TP2_SS<-lmer(Sym.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="SS"),])
summary(Tol_Sym.lme_TP2_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Sym.prop ~ Origin + (1 | Genotype)
Data: Therm_TP2[which(Therm_TP2$Site == "SS"), ]
REML criterion at convergence: 3.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.98373 -0.46709 -0.03333 0.50742 1.82777
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.04792 0.2189
Residual 0.04392 0.2096
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.62741 0.14011 2.42980 4.478 0.032 *
OriginTransplant -0.10272 0.08556 20.00000 -1.201 0.244
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.305eta_squared(Tol_Sym.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.07 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP2_TolSym.sum<-summarySE(Therm_TP2, measurevar="Sym.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP2_TolSym.plot<-ggplot(TP2_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Symbiont Retention", colour="Origin")+
ylim(0, 1); TP2_TolSym.plot
Chlorophyll
#Check normality
hist(Therm_TP2$Chl.prop)
shapiro.test(Therm_TP2$Chl.prop)
Shapiro-Wilk normality test
data: Therm_TP2$Chl.prop
W = 0.97677, p-value = 0.4523#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Chl.lme_TP2<-lmer(Chl.prop~Origin*Site+(1|Genotype), data=Therm_TP2)
##Check residuals
Tol_Chl.lme_res_TP2 <- simulateResiduals(fittedModel = Tol_Chl.lme_TP2, plot = F)
plot(Tol_Chl.lme_res_TP2)
##Model results
summary(Tol_Chl.lme_TP2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl.prop ~ Origin * Site + (1 | Genotype)
Data: Therm_TP2
REML criterion at convergence: -81
Scaled residuals:
Min 1Q Median 3Q Max
-2.0024 -0.7622 -0.1174 0.6824 2.9666
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.006146 0.07840
Residual 0.006538 0.08086
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.31077 0.05093 2.81331 6.102 0.0106 *
OriginTransplant -0.03844 0.03301 42.00000 -1.165 0.2508
SiteSS -0.06514 0.03301 42.00000 -1.973 0.0550 .
OriginTransplant:SiteSS 0.06166 0.04668 42.00000 1.321 0.1937
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.324
SiteSS -0.324 0.500
OrgnTrn:SSS 0.229 -0.707 -0.707eta_squared(Tol_Chl.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 2.53e-03 | [0.00, 1.00]
Site | 0.05 | [0.00, 1.00]
Origin:Site | 0.04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Tol_Chl.lme_TP2_KL<-lmer(Chl.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="KL"),])
summary(Tol_Chl.lme_TP2_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl.prop ~ Origin + (1 | Genotype)
Data: Therm_TP2[which(Therm_TP2$Site == "KL"), ]
REML criterion at convergence: -39.9
Scaled residuals:
Min 1Q Median 3Q Max
-0.9725 -0.7459 -0.3411 0.6270 2.6035
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.006040 0.07772
Residual 0.006269 0.07918
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.31077 0.05036 2.48246 6.171 0.0146 *
OriginTransplant -0.03844 0.03232 20.00001 -1.189 0.2483
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.321eta_squared(Tol_Chl.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.07 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_Chl.lme_TP2_SS<-lmer(Chl.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="SS"),])
summary(Tol_Chl.lme_TP2_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Chl.prop ~ Origin + (1 | Genotype)
Data: Therm_TP2[which(Therm_TP2$Site == "SS"), ]
REML criterion at convergence: -46
Scaled residuals:
Min 1Q Median 3Q Max
-2.2119 -0.6418 0.2701 0.7660 1.5135
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.009505 0.09749
Residual 0.004441 0.06664
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.24563 0.05948 2.22611 4.129 0.0448 *
OriginTransplant 0.02322 0.02720 20.00000 0.853 0.4035
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.229eta_squared(Tol_Chl.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP2_TolChl.sum<-summarySE(Therm_TP2, measurevar="Chl.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP2_TolChl.plot<-ggplot(TP2_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
ylim(0, 0.35); TP2_TolChl.plot
FvFm
#Check normality
hist(Therm_TP2$PAM.prop)
shapiro.test(Therm_TP2$PAM.prop)
Shapiro-Wilk normality test
data: Therm_TP2$PAM.prop
W = 0.73527, p-value = 5.896e-08#Not normal
hist(log(Therm_TP2$PAM.prop+1))
shapiro.test(log(Therm_TP2$PAM.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP2$PAM.prop + 1)
W = 0.70205, p-value = 1.443e-08##Still not normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_PAM.lme_TP2<-lmer(PAM.prop~Origin*Site+(1|Genotype), data=Therm_TP2)
##Check residuals
Tol_PAM.lme_res_TP2 <- simulateResiduals(fittedModel = Tol_PAM.lme_TP2, plot = F)
plot(Tol_PAM.lme_res_TP2)
##Model results
summary(Tol_PAM.lme_TP2)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin * Site + (1 | Genotype)
Data: Therm_TP2
REML criterion at convergence: -102.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.8326 -0.3753 -0.0042 0.5350 1.7496
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001675 0.04093
Residual 0.004192 0.06475
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.85622 0.03013 3.92146 28.419 1.09e-05 ***
OriginTransplant 0.07526 0.02643 42.00000 2.847 0.00680 **
SiteSS 0.06487 0.02643 42.00000 2.454 0.01834 *
OriginTransplant:SiteSS -0.10206 0.03738 42.00000 -2.730 0.00921 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.439
SiteSS -0.439 0.500
OrgnTrn:SSS 0.310 -0.707 -0.707eta_squared(Tol_PAM.lme_TP2)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.04 | [0.00, 1.00]
Site | 0.01 | [0.00, 1.00]
Origin:Site | 0.15 | [0.02, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Not normal, but model residuals are OK.
Effect size of Origin for each Site
##KL
Tol_PAM.lme_TP2_KL<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="KL"),])
summary(Tol_PAM.lme_TP2_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin + (1 | Genotype)
Data: Therm_TP2[which(Therm_TP2$Site == "KL"), ]
REML criterion at convergence: -40.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.9208 -0.4020 0.2520 0.5255 1.0060
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.003195 0.05653
Residual 0.006508 0.08067
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.85622 0.04009 2.88245 21.356 0.00029 ***
OriginTransplant 0.07526 0.03293 19.99994 2.285 0.03336 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.411eta_squared(Tol_PAM.lme_TP2_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.21 | [0.01, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_PAM.lme_TP2_SS<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="SS"),])
summary(Tol_PAM.lme_TP2_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin + (1 | Genotype)
Data: Therm_TP2[which(Therm_TP2$Site == "SS"), ]
REML criterion at convergence: -71.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.04910 -0.74292 -0.04708 0.53594 2.26126
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0004937 0.02222
Residual 0.0016296 0.04037
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.92109 0.01733 3.31062 53.146 5.72e-06 ***
OriginTransplant -0.02680 0.01648 20.00000 -1.626 0.12
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.475eta_squared(Tol_PAM.lme_TP2_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.12 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP2_TolPAM.sum<-summarySE(Therm_TP2, measurevar="PAM.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP2_TolPAM.plot<-ggplot(TP2_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Photochemical Efficiency Retention", colour="Origin")+
ylim(0.6, 1)+
annotate("text", x=1, y=1, label="*", size=sig.sz, fontface="bold"); TP2_TolPAM.plot
TP3
##Subset Timepoint 3
Therm_TP3<-subset(Therm_H, TimeP=="TP3")Symbionts
#Check normality
hist(Therm_TP3$Sym.prop)
shapiro.test(Therm_TP3$Sym.prop)
Shapiro-Wilk normality test
data: Therm_TP3$Sym.prop
W = 0.78653, p-value = 6.531e-07#Not normal
hist(log(Therm_TP3$Sym.prop+1))
shapiro.test(log(Therm_TP3$Sym.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP3$Sym.prop + 1)
W = 0.78571, p-value = 6.267e-07##Still not normal but less skewed
##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Sym.lme_TP3<-lmer(log(Sym.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP3)
##Check residuals
Tol_Sym.lme_res_TP3 <- simulateResiduals(fittedModel = Tol_Sym.lme_TP3, plot = F)
plot(Tol_Sym.lme_res_TP3)
##Model results
summary(Tol_Sym.lme_TP3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin * Site + (1 | Genotype)
Data: Therm_TP3
REML criterion at convergence: -108.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.1559 -0.5389 0.3778 0.5963 1.6051
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 8.41e-05 0.00917
Residual 3.90e-03 0.06245
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.59780 0.01879 16.87446 31.815 <2e-16 ***
OriginTransplant 0.03978 0.02550 42.00000 1.560 0.1262
SiteSS 0.05892 0.02550 42.00000 2.311 0.0258 *
OriginTransplant:SiteSS -0.03130 0.03606 42.00000 -0.868 0.3903
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.678
SiteSS -0.678 0.500
OrgnTrn:SSS 0.480 -0.707 -0.707eta_squared(Tol_Sym.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.04 | [0.00, 1.00]
Site | 0.12 | [0.01, 1.00]
Origin:Site | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Not normal, but model residuals are OK.
Effect size of Origin for each Site
##KL
Tol_Sym.lme_TP3_KL<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="KL"),])
summary(Tol_Sym.lme_TP3_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP3[which(Therm_TP3$Site == "KL"), ]
REML criterion at convergence: -48.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.66523 -0.53461 0.01898 0.71797 1.50619
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0003833 0.01958
Residual 0.0048325 0.06952
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.59780 0.02303 5.00842 25.955 1.56e-06 ***
OriginTransplant 0.03978 0.02838 20.00000 1.402 0.176
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.616eta_squared(Tol_Sym.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.09 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_Sym.lme_TP3_SS<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Tol_Sym.lme_TP3_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP3[which(Therm_TP3$Site == "SS"), ]
REML criterion at convergence: -61.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.6314 -0.4640 0.5270 0.5670 0.6869
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.000000 0.00000
Residual 0.002812 0.05302
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.656722 0.015307 22.000000 42.904 <2e-16 ***
OriginTransplant 0.008483 0.021647 22.000000 0.392 0.699
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Tol_Sym.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 6.93e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP3_TolSym.sum<-summarySE(Therm_TP3, measurevar="Sym.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP3_TolSym.plot<-ggplot(TP3_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Symbiont Retention", colour="Origin")+
ylim(0, 1); TP3_TolSym.plot
Chlorophyll
#Check normality
hist(Therm_TP3$Chl.prop)
shapiro.test(Therm_TP3$Chl.prop)
Shapiro-Wilk normality test
data: Therm_TP3$Chl.prop
W = 0.89534, p-value = 0.0004461#Not normal
hist(log(Therm_TP3$Chl.prop+1))
shapiro.test(log(Therm_TP3$Chl.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP3$Chl.prop + 1)
W = 0.9211, p-value = 0.003251##Still not normal but less skewed
##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Chl.lme_TP3<-lmer(log(Chl.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP3)boundary (singular) fit: see help('isSingular')##Check residuals
Tol_Chl.lme_res_TP3 <- simulateResiduals(fittedModel = Tol_Chl.lme_TP3, plot = F)
plot(Tol_Chl.lme_res_TP3)
##Model results
summary(Tol_Chl.lme_TP3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin * Site + (1 | Genotype)
Data: Therm_TP3
REML criterion at convergence: -47.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.7095 -0.6080 -0.1405 0.4999 1.7650
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.01594 0.1262
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.43851 0.03644 44.00000 12.032 1.65e-15 ***
OriginTransplant 0.03181 0.05154 44.00000 0.617 0.540
SiteSS 0.03852 0.05154 44.00000 0.747 0.459
OriginTransplant:SiteSS -0.02511 0.07289 44.00000 -0.344 0.732
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.707
SiteSS -0.707 0.500
OrgnTrn:SSS 0.500 -0.707 -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Tol_Chl.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 6.31e-03 | [0.00, 1.00]
Site | 0.01 | [0.00, 1.00]
Origin:Site | 2.69e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Not normal, but model residuals are OK.
Effect size of Origin for each Site
##KL
Tol_Chl.lme_TP3_KL<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="KL"),])
summary(Tol_Chl.lme_TP3_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP3[which(Therm_TP3$Site == "KL"), ]
REML criterion at convergence: -35
Scaled residuals:
Min 1Q Median 3Q Max
-2.1966 -0.5212 -0.1283 0.4729 1.9007
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002703 0.05199
Residual 0.008473 0.09205
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.43851 0.04009 3.25876 10.938 0.00112 **
OriginTransplant 0.03181 0.03758 20.00000 0.847 0.40725
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.469eta_squared(Tol_Chl.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_Chl.lme_TP3_SS<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Tol_Chl.lme_TP3_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP3[which(Therm_TP3$Site == "SS"), ]
REML criterion at convergence: -17.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.4740 -0.7462 -0.2782 0.6974 1.4761
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 3.210e-22 1.792e-11
Residual 2.144e-02 1.464e-01
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.477025 0.042265 22.000000 11.286 1.28e-10 ***
OriginTransplant 0.006709 0.059772 22.000000 0.112 0.912
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Tol_Chl.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 5.72e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP3_TolChl.sum<-summarySE(Therm_TP3, measurevar="Chl.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP3_TolChl.plot<-ggplot(TP3_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
ylim(0, 1); TP3_TolChl.plot
FvFm
#Check normality
hist(Therm_TP3$PAM.prop)
shapiro.test(Therm_TP3$PAM.prop)
Shapiro-Wilk normality test
data: Therm_TP3$PAM.prop
W = 0.96358, p-value = 0.1408#Normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_PAM.lme_TP3<-lmer(PAM.prop~Origin*Site+(1|Genotype), data=Therm_TP3)
##Check residuals
Tol_PAM.lme_res_TP3 <- simulateResiduals(fittedModel = Tol_PAM.lme_TP3, plot = F)
plot(Tol_PAM.lme_res_TP3)
##Model results
summary(Tol_PAM.lme_TP3)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin * Site + (1 | Genotype)
Data: Therm_TP3
REML criterion at convergence: -172.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.29399 -0.71463 0.01706 0.83761 1.77082
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 3.177e-05 0.005637
Residual 9.061e-04 0.030101
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.954325 0.009279 14.520200 102.850 <2e-16 ***
OriginTransplant 0.009025 0.012289 42.000000 0.734 0.467
SiteSS 0.003975 0.012289 42.000000 0.323 0.748
OriginTransplant:SiteSS -0.019133 0.017379 42.000000 -1.101 0.277
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.662
SiteSS -0.662 0.500
OrgnTrn:SSS 0.468 -0.707 -0.707eta_squared(Tol_PAM.lme_TP3)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 9.25e-05 | [0.00, 1.00]
Site | 9.76e-03 | [0.00, 1.00]
Origin:Site | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Tol_PAM.lme_TP3_KL<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="KL"),])
summary(Tol_PAM.lme_TP3_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin + (1 | Genotype)
Data: Therm_TP3[which(Therm_TP3$Site == "KL"), ]
REML criterion at convergence: -87.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.4364 -0.5727 0.1072 0.8113 1.2811
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 3.996e-05 0.006322
Residual 8.452e-04 0.029072
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.954325 0.009152 5.656770 104.28 1.65e-10 ***
OriginTransplant 0.009025 0.011869 20.001145 0.76 0.456
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.648eta_squared(Tol_PAM.lme_TP3_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_PAM.lme_TP3_SS<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Tol_PAM.lme_TP3_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin + (1 | Genotype)
Data: Therm_TP3[which(Therm_TP3$Site == "SS"), ]
REML criterion at convergence: -84.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.6542 -0.7178 -0.1194 0.6304 1.6515
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.0000000 0.00000
Residual 0.0009841 0.03137
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.958300 0.009056 22.000000 105.820 <2e-16 ***
OriginTransplant -0.010108 0.012807 22.000000 -0.789 0.438
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Tol_PAM.lme_TP3_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP3_TolPAM.sum<-summarySE(Therm_TP3, measurevar="PAM.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP3_TolPAM.plot<-ggplot(TP3_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Photochemical Efficiency Retention", colour="Origin")+
ylim(0.7, 1); TP3_TolPAM.plot
TP4
##Subset Timepoint 4
Therm_TP4<-subset(Therm_H, TimeP=="TP4")Symbionts
#Check normality
hist(Therm_TP4$Sym.prop)
shapiro.test(Therm_TP4$Sym.prop)
Shapiro-Wilk normality test
data: Therm_TP4$Sym.prop
W = 0.93453, p-value = 0.01005#Not normal
hist(log(Therm_TP4$Sym.prop+1))
shapiro.test(log(Therm_TP4$Sym.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP4$Sym.prop + 1)
W = 0.95458, p-value = 0.061#Normal
##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Sym.lme_TP4<-lmer(log(Sym.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP4)
##Check residuals
Tol_Sym.lme_res_TP4 <- simulateResiduals(fittedModel = Tol_Sym.lme_TP4, plot = F)
plot(Tol_Sym.lme_res_TP4)
##Model results
summary(Tol_Sym.lme_TP4)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin * Site + (1 | Genotype)
Data: Therm_TP4
REML criterion at convergence: -44.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.5412 -0.7855 -0.1534 0.5518 2.2412
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.001295 0.03598
Residual 0.016289 0.12763
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.24607 0.04230 9.94436 5.818 0.000173 ***
OriginTransplant 0.04098 0.05210 42.00002 0.787 0.435975
SiteSS -0.03092 0.05210 42.00002 -0.593 0.556032
OriginTransplant:SiteSS -0.03256 0.07369 42.00002 -0.442 0.660816
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.616
SiteSS -0.616 0.500
OrgnTrn:SSS 0.436 -0.707 -0.707eta_squared(Tol_Sym.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 0.01 | [0.00, 1.00]
Site | 0.04 | [0.00, 1.00]
Origin:Site | 4.63e-03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Tol_Sym.lme_TP4_KL<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="KL"),])
summary(Tol_Sym.lme_TP4_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP4[which(Therm_TP4$Site == "KL"), ]
REML criterion at convergence: -26.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.3745 -0.8456 0.0271 0.5432 2.0020
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.002906 0.05391
Residual 0.012909 0.11362
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.24607 0.04522 3.63655 5.442 0.00725 **
OriginTransplant 0.04098 0.04638 20.00000 0.884 0.38746
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.513eta_squared(Tol_Sym.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_Sym.lme_TP4_SS<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Tol_Sym.lme_TP4_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Sym.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP4[which(Therm_TP4$Site == "SS"), ]
REML criterion at convergence: -19.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.19030 -0.85642 -0.01412 0.53810 2.27217
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.00000 0.0000
Residual 0.01944 0.1394
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.215142 0.040247 22.000000 5.346 2.29e-05 ***
OriginTransplant 0.008418 0.056918 22.000000 0.148 0.884
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Tol_Sym.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 9.93e-04 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP4_TolSym.sum<-summarySE(Therm_TP4, measurevar="Sym.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP4_TolSym.plot<-ggplot(TP4_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Symbiont Retention", colour="Origin")+
ylim(0, 1); TP4_TolSym.plot
Chlorophyll
#Check normality
hist(Therm_TP4$Chl.prop)
shapiro.test(Therm_TP4$Chl.prop)
Shapiro-Wilk normality test
data: Therm_TP4$Chl.prop
W = 0.94204, p-value = 0.02131#Not normal
hist(log(Therm_TP4$Chl.prop+1))
shapiro.test(log(Therm_TP4$Chl.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP4$Chl.prop + 1)
W = 0.9512, p-value = 0.04824##Nearly normal
##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Chl.lme_TP4<-lmer(log(Chl.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP4)boundary (singular) fit: see help('isSingular')##Check residuals
Tol_Chl.lme_res_TP4 <- simulateResiduals(fittedModel = Tol_Chl.lme_TP4, plot = F)
plot(Tol_Chl.lme_res_TP4)
##Model results
summary(Tol_Chl.lme_TP4)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin * Site + (1 | Genotype)
Data: Therm_TP4
REML criterion at convergence: -139.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.6397 -0.7647 -0.1492 0.5006 2.4251
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.000000 0.00000
Residual 0.001803 0.04246
Number of obs: 47, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.09448 0.01226 43.00000 7.707 1.24e-09 ***
OriginTransplant -0.01266 0.01734 43.00000 -0.730 0.469
SiteSS -0.02659 0.01773 43.00000 -1.500 0.141
OriginTransplant:SiteSS 0.02939 0.02479 43.00000 1.185 0.242
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.707
SiteSS -0.692 0.489
OrgnTrn:SSS 0.494 -0.699 -0.715
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Tol_Chl.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 6.26e-04 | [0.00, 1.00]
Site | 0.02 | [0.00, 1.00]
Origin:Site | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Tol_Chl.lme_TP4_KL<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="KL"),])boundary (singular) fit: see help('isSingular')summary(Tol_Chl.lme_TP4_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP4[which(Therm_TP4$Site == "KL"), ]
REML criterion at convergence: -76.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.5133 -0.5558 -0.1258 0.5428 2.7208
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.000000 0.00000
Residual 0.001433 0.03785
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.09448 0.01093 22.00000 8.647 1.59e-08 ***
OriginTransplant -0.01266 0.01545 22.00000 -0.819 0.421
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.707
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Tol_Chl.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_Chl.lme_TP4_SS<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="SS"),])boundary (singular) fit: see help('isSingular')summary(Tol_Chl.lme_TP4_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: log(Chl.prop + 1) ~ Origin + (1 | Genotype)
Data: Therm_TP4[which(Therm_TP4$Site == "SS"), ]
REML criterion at convergence: -64.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.4873 -0.7211 -0.1933 0.5918 2.1830
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.000000 0.00000
Residual 0.002192 0.04681
Number of obs: 23, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.06788 0.01412 21.00000 4.809 9.41e-05 ***
OriginTransplant 0.01673 0.01954 21.00000 0.856 0.402
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.722
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')eta_squared(Tol_Chl.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP4_TolChl.sum<-summarySE(Therm_TP4, measurevar="Chl.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP4_TolChl.plot<-ggplot(TP4_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
ylim(0, 0.35); TP4_TolChl.plot
FvFm
#Check normality
hist(Therm_TP4$PAM.prop)
shapiro.test(Therm_TP4$PAM.prop)
Shapiro-Wilk normality test
data: Therm_TP4$PAM.prop
W = 0.86379, p-value = 5.159e-05#Not normal
hist(log(Therm_TP4$PAM.prop+1))
shapiro.test(log(Therm_TP4$PAM.prop+1))
Shapiro-Wilk normality test
data: log(Therm_TP4$PAM.prop + 1)
W = 0.84113, p-value = 1.275e-05##Still not normal
##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_PAM.lme_TP4<-lmer(PAM.prop~Origin*Site+(1|Genotype), data=Therm_TP4)
##Check residuals
Tol_PAM.lme_res_TP4 <- simulateResiduals(fittedModel = Tol_PAM.lme_TP4, plot = F)
plot(Tol_PAM.lme_res_TP4)
##Model results
summary(Tol_PAM.lme_TP4)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin * Site + (1 | Genotype)
Data: Therm_TP4
REML criterion at convergence: -60.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.1005 -0.4066 -0.0093 0.5802 2.4408
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.01070 0.1035
Residual 0.01028 0.1014
Number of obs: 48, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.74804 0.06651 2.73320 11.247 0.00228 **
OriginTransplant 0.03239 0.04138 42.00000 0.783 0.43818
SiteSS 0.02783 0.04138 42.00000 0.672 0.50502
OriginTransplant:SiteSS -0.05803 0.05852 42.00000 -0.991 0.32713
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) OrgnTr SiteSS
OrgnTrnspln -0.311
SiteSS -0.311 0.500
OrgnTrn:SSS 0.220 -0.707 -0.707eta_squared(Tol_PAM.lme_TP4)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-------------------------------------------
Origin | 3.17e-04 | [0.00, 1.00]
Site | 3.92e-05 | [0.00, 1.00]
Origin:Site | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].Effect size of Origin for each Site
##KL
Tol_PAM.lme_TP4_KL<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="KL"),])
summary(Tol_PAM.lme_TP4_KL)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin + (1 | Genotype)
Data: Therm_TP4[which(Therm_TP4$Site == "KL"), ]
REML criterion at convergence: -30.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.84954 -0.38862 -0.07926 0.54042 2.60201
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.011716 0.10824
Residual 0.009227 0.09606
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.74804 0.06837 2.37263 10.941 0.00438 **
OriginTransplant 0.03239 0.03922 20.00001 0.826 0.41856
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.287eta_squared(Tol_PAM.lme_TP4_KL)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.03 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##SS
Tol_PAM.lme_TP4_SS<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="SS"),])
summary(Tol_PAM.lme_TP4_SS)Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: PAM.prop ~ Origin + (1 | Genotype)
Data: Therm_TP4[which(Therm_TP4$Site == "SS"), ]
REML criterion at convergence: -25.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.08840 -0.38585 0.06028 0.59276 2.02689
Random effects:
Groups Name Variance Std.Dev.
Genotype (Intercept) 0.008458 0.09197
Residual 0.012219 0.11054
Number of obs: 24, groups: Genotype, 3
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.77587 0.06195 2.65242 12.524 0.00196 **
OriginTransplant -0.02563 0.04513 20.00000 -0.568 0.57634
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
OrgnTrnspln -0.364eta_squared(Tol_PAM.lme_TP4_SS)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
Origin | 0.02 | [0.00, 1.00]
- One-sided CIs: upper bound fixed at [1.00].##Summary statistics by Site and Origin
TP4_TolPAM.sum<-summarySE(Therm_TP4, measurevar="PAM.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)
##Plot Average Retention across Treatments
TP4_TolPAM.plot<-ggplot(TP4_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="Photochemical Efficiency Retention", colour="Origin")+
ylim(0.6, 1); TP4_TolPAM.plot
Effect Size over Time
##Dataframe of effect size results
Tol.ES<-data.frame(TimeP=c(rep("TP1",6), rep("TP2",6), rep("TP3",6), rep("TP4",6)),
Site=c(rep(c("KL", "SS"),12)),
Metric=c(rep(c("Sym.prop", "Sym.prop", "Chl.prop", "Chl.prop", "PAM.prop", "PAM.prop"),4)),
EtaSq=c(eta_squared(Tol_Sym.lme_TP1_KL)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP1_SS)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP1_KL)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP1_SS)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP1_KL)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP1_SS)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP2_KL)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP2_SS)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP2_KL)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP2_SS)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP2_KL)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP2_SS)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP3_KL)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP3_SS)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP3_KL)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP3_SS)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP3_KL)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP3_SS)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP4_KL)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP4_SS)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP4_KL)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP4_SS)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP4_KL)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP4_SS)$Eta2_partial),
Pvalue=c(summary(Tol_Sym.lme_TP1_KL)$coefficients[10],
summary(Tol_Sym.lme_TP1_SS)$coefficients[10],
summary(Tol_Chl.lme_TP1_KL)$coefficients[10],
summary(Tol_Chl.lme_TP1_SS)$coefficients[10],
summary(Tol_PAM.lme_TP1_KL)$coefficients[10],
summary(Tol_PAM.lme_TP1_SS)$coefficients[10],
summary(Tol_Sym.lme_TP2_KL)$coefficients[10],
summary(Tol_Sym.lme_TP2_SS)$coefficients[10],
summary(Tol_Chl.lme_TP2_KL)$coefficients[10],
summary(Tol_Chl.lme_TP2_SS)$coefficients[10],
summary(Tol_PAM.lme_TP2_KL)$coefficients[10],
summary(Tol_PAM.lme_TP2_SS)$coefficients[10],
summary(Tol_Sym.lme_TP3_KL)$coefficients[10],
summary(Tol_Sym.lme_TP3_SS)$coefficients[10],
summary(Tol_Chl.lme_TP3_KL)$coefficients[10],
summary(Tol_Chl.lme_TP3_SS)$coefficients[10],
summary(Tol_PAM.lme_TP3_KL)$coefficients[10],
summary(Tol_PAM.lme_TP3_SS)$coefficients[10],
summary(Tol_Sym.lme_TP4_KL)$coefficients[10],
summary(Tol_Sym.lme_TP4_SS)$coefficients[10],
summary(Tol_Chl.lme_TP4_KL)$coefficients[10],
summary(Tol_Chl.lme_TP4_SS)$coefficients[10],
summary(Tol_PAM.lme_TP4_KL)$coefficients[10],
summary(Tol_PAM.lme_TP4_SS)$coefficients[10]))
Tol.ES$Sig<-ifelse(Tol.ES$Pvalue<0.001, "***", ifelse(Tol.ES$Pvalue<0.01, "**", ifelse(Tol.ES$Pvalue<0.05, "*", ifelse(Tol.ES$Pvalue<0.1, "-", NA))))Symbionts
Tol_Sym.ES.plot<-ggplot(Tol.ES[which(Tol.ES$Metric=="Sym.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Symbiont Retention")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.4)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Tol_Sym.ES.plot
Chlorophyll
Tol_Chl.ES.plot<-ggplot(Tol.ES[which(Tol.ES$Metric=="Chl.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Chlorophyll Retention")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.4)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Tol_Chl.ES.plot
FvFm
Tol_PAM.ES.plot<-ggplot(Tol.ES[which(Tol.ES$Metric=="PAM.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Photochemical Efficiency Retention")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.4)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Tol_PAM.ES.plot
Trade Offs
Average Performance
Calculating the average of Growth (TP1 to TP2) and Thermal Tolerance (Retention of Symbionts, Chlorophyll, and Photochemical Efficiency at TP1) for each Set (Site, Genotype, Origin)
Growth
##Average
names(Growth) [1] "Site" "ID" "Orig" "Origin" "Genotype" "TL.TP1_cm" "TL.TP2_cm"
[8] "Set" "Site.Orig" "TP1" "TP2" "TP1.2_days" "Ext_cm" "TLE_cm.day"Growth_Set<-aggregate(Growth$TLE_cm.day, list(Growth$Site, Growth$Genotype, Growth$Orig), mean)
names(Growth_Set)<-c("Site", "Genotype", "Orig", "TLE_cm.day")
##Add Standard Error
Growth_Set$TLE_se<-aggregate(Growth$TLE_cm.day, list(Growth$Site, Growth$Genotype, Growth$Orig), std.error)[,4]Tolerance
##Average
names(Therm_TP1) [1] "TimeP" "Site" "Genotype" "Origin" "ID"
[6] "RandN" "Treat" "Treatment" "Orig" "Set"
[11] "Site.Orig" "SA_cm2" "Chl_ug.cm2" "Sym10.6_cm2" "Fv_Fm"
[16] "Chl_ug.cm2_C" "Sym10.6_cm2_C" "Fv_Fm_C" "Chl.prop" "Sym.prop"
[21] "PAM.prop" Therm_Set<-aggregate(Therm_TP1[,c(19:21)], list(Therm_TP1$Site, Therm_TP1$Genotype, Therm_TP1$Orig), mean)
names(Therm_Set)<-c("Site", "Genotype", "Orig", "Chl.prop", "Sym.prop", "PAM.prop")
##Add Standard Error
Therm_Set$Chl_se<-aggregate(Therm_TP1$Chl.prop, list(Therm_TP1$Site, Therm_TP1$Genotype, Therm_TP1$Orig), std.error)[,4]
Therm_Set$Sym_se<-aggregate(Therm_TP1$Sym.prop, list(Therm_TP1$Site, Therm_TP1$Genotype, Therm_TP1$Orig), std.error)[,4]
Therm_Set$PAM_se<-aggregate(Therm_TP1$PAM.prop, list(Therm_TP1$Site, Therm_TP1$Genotype, Therm_TP1$Orig), std.error)[,4]Merge Growth and Tolerance
Perf_Set<-merge(Growth_Set, Therm_Set, all=TRUE)
Perf_Set$Set<-paste(Perf_Set$Site, Perf_Set$Genotype, Perf_Set$Orig, sep=".")
##Set factor variables
Perf_Set$Site<-factor(Perf_Set$Site, levels=c("KL", "SS"))
Perf_Set$Genotype<-factor(Perf_Set$Genotype, levels=c("AC8", "AC10", "AC12"))
Perf_Set$Orig<-factor(Perf_Set$Orig, levels=c("N", "T"))
##Add a Sample Set Variable
Perf_Set$Set<-paste(Perf_Set$Site, Perf_Set$Genotype, Perf_Set$Orig, sep=".")
Perf_Set$Set<-factor(Perf_Set$Set)
##Add Site.Orig variable
Perf_Set$Site.Orig<-paste(Perf_Set$Site, Perf_Set$Orig, sep=".")
Perf_Set$Site.Orig<-factor(Perf_Set$Site.Orig, levels=c("KL.N", "KL.T", "SS.N", "SS.T"))
Correlation between Growth and Tolerance
Symbiont Retention
cor.test(Perf_Set$TLE_cm.day, Perf_Set$Sym.prop, method="spearman")
Spearman's rank correlation rho
data: Perf_Set$TLE_cm.day and Perf_Set$Sym.prop
S = 462, p-value = 0.03733
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.6153846 TLE_Sym.plot<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=Sym.prop)) +
geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
geom_point(aes(colour=Site.Orig),size=point.sz)+
scale_colour_manual(values=Orig.colors.o)+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x=expression(paste('Growth Rate (cm day'^-1*")")),
y="Symbiont Retention",
colour="Site Origin")+
annotate("text", x = 0.07, y = 0, label=expression(bolditalic(paste(r[S], " = -0.615, p = 0.037"))), size=sig.sz, hjust = 0); TLE_Sym.plot
Chlorophyll Retention
cor.test(Perf_Set$TLE_cm.day, Perf_Set$Chl.prop, method="spearman")
Spearman's rank correlation rho
data: Perf_Set$TLE_cm.day and Perf_Set$Chl.prop
S = 454, p-value = 0.04884
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.5874126 TLE_Chl.plot<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=Chl.prop)) +
geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
geom_point(aes(colour=Site.Orig),size=point.sz)+
scale_colour_manual(values=Orig.colors.o)+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x=expression(paste('Growth Rate (cm day'^-1*")")),
y="Chlorophyll Retention",
colour="Site Origin")+
annotate("text", x = 0.07, y = 0, label=expression(bolditalic(paste(r[S], " = -0.587, p = 0.049"))), size=sig.sz, hjust = 0); TLE_Chl.plot
FvFm Retention
cor.test(Perf_Set$TLE_cm.day, Perf_Set$PAM.prop, method="spearman")
Spearman's rank correlation rho
data: Perf_Set$TLE_cm.day and Perf_Set$PAM.prop
S = 480, p-value = 0.01883
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
-0.6783217 TLE_PAM.plot<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=PAM.prop)) +
geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
geom_point(aes(colour=Site.Orig),size=point.sz)+
scale_colour_manual(values=Orig.colors.o)+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x=expression(paste('Growth Rate (cm day'^-1*")")),
y="Photo. Effic. Retention",
colour="Site Origin")+
annotate("text", x = 0.07, y = 0.74, label=expression(bolditalic(paste(r[S], " = -0.678, p = 0.019"))), size=sig.sz, hjust = 0); TLE_PAM.plot
Figures
Figure 3 Growth and Tolerance ES
Adjust Figures for Panel
##Growth
TP1.2_Growth.plot.cut<-ggplot(TP1.2_Growth.sum, aes(x=Site, y=TLE_cm.day, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=TLE_cm.day-se, ymax=TLE_cm.day+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"), legend.position=c(0.2, 0.8))+
labs(x="Site and Origin", y=expression(paste('Growth Rate (cm day'^-1*")")), colour="Origin")+
ylim(0, 0.3)+
annotate("text", x=2, y=0.24, label="*", size=sig.sz, fontface="bold")+
geom_bracket(xmin=1, xmax=2, y.position=0.01, label.size=levels.sz, label="**", inherit.aes = FALSE)
##FvFm Retention
Tol_PAM.ES.plot.cut<-ggplot(Tol.ES[which(Tol.ES$Metric=="PAM.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Photochemical Efficiency")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"), legend.position=c(0.8, 0.8))+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.35)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")
##Symbiont Retention
Tol_Sym.ES.plot.cut<-ggplot(Tol.ES[which(Tol.ES$Metric=="Sym.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Symbionts")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.35)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")
##Chlorophyll Retention
Tol_Chl.ES.plot.cut<-ggplot(Tol.ES[which(Tol.ES$Metric=="Chl.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
geom_bar(stat="identity", position=position_dodge())+
scale_fill_manual(values=Site.colors.o)+
theme_classic()+
ggtitle("Chlorophyll")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
ylim(0, 0.35)+
geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")
Figure 3
##Create Panel
Performance_fig<-plot_grid(TP1.2_Growth.plot.cut, Tol_PAM.ES.plot.cut,
Tol_Sym.ES.plot.cut, Tol_Chl.ES.plot.cut,
rel_widths=c(1, 1, 1, 1),
rel_heights = c(1, 1, 1, 1),
nrow=2, ncol=2, byrow=T, labels = c("A", "B", "C", "D"))Warning: Removed 6 rows containing missing values (`geom_text()`).Warning: Removed 7 rows containing missing values (`geom_text()`).Warning: Removed 8 rows containing missing values (`geom_text()`).##Save Figure
ggsave(filename="Figures/03_Performance/Fig3_Performance.png", plot=Performance_fig, dpi=300, width=10, height=8, units="in")
Figure 4 Growth vs Tolerance
Adjust Figures for Panel
##Growth vs FvFm Retention
TLE_PAM.plot.cut<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=PAM.prop)) +
geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
geom_point(aes(colour=Site.Orig),size=point.sz)+
scale_colour_manual(values=Orig.colors.o)+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
labs(x=expression(paste('Growth Rate (cm day'^-1*")")),
y="Photo. Effic. Retention",
colour="Site Origin")+
annotate("text", x = 0.07, y = 0.75, label=expression(bolditalic(paste(r[S], " = -0.678, p = 0.019"))), size=sig.sz, hjust = 0)
##Growth vs Symbiont Retention
TLE_Sym.plot.cut<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=Sym.prop)) +
geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
geom_point(aes(colour=Site.Orig),size=point.sz)+
scale_colour_manual(values=Orig.colors.o)+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
labs(x=expression(paste('Growth Rate (cm day'^-1*")")),
y="Symbiont Retention",
colour="Site Origin")+
annotate("text", x = 0.07, y = 0, label=expression(bolditalic(paste(r[S], " = -0.615, p = 0.037"))), size=sig.sz, hjust = 0)Figure 4
##Create Panel
TradeOff_fig<-plot_grid(TLE_PAM.plot.cut, TLE_Sym.plot.cut, TLE_Chl.plot,
rel_widths=c(.75, .75, 1),
rel_heights = c(1, 1, 1),
nrow=1, ncol=3, byrow=T, labels = c("A", "B", "C"))`geom_smooth()` using formula = 'y ~ x'Warning: is.na() applied to non-(list or vector) of type 'expression'`geom_smooth()` using formula = 'y ~ x'Warning: is.na() applied to non-(list or vector) of type 'expression'`geom_smooth()` using formula = 'y ~ x'Warning: is.na() applied to non-(list or vector) of type 'expression'##Save Figure
ggsave(filename="Figures/03_Performance/Fig4_TradeOff.png", plot=TradeOff_fig, dpi=300, width=12, height=4, units="in")
Figure S4 Initial Heat Assay
Adjust Figures for Panel
##FvFm
IN_PAM.plot.cut<-ggplot(IN_PAM.sum, aes(x=Site.Treat, y=Fv_Fm, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Fv_Fm-se, ymax=Fv_Fm+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
scale_shape_manual(values=c(1,16))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="Fv/Fm", colour="Site")+
ylim(0.5, 0.655)
IN_TolPAM.plot.cut<-ggplot(IN_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="Retention", colour="Site")+
ylim(0.75, 1)+
annotate("text", x=1.5, y=0.95, label="*", size=sig.sz, fontface="bold")
##Symbionts
IN_Sym.plot.cut<-ggplot(IN_Sym.sum, aes(x=Site.Treat, y=Sym10.6_cm2, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Sym10.6_cm2-se, ymax=Sym10.6_cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
scale_shape_manual(values=c(1,16))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y=expression(paste('Symbionts ('*10^6,'cells cm'^-2*")")), colour="Site")+
ylim(0, 1)
IN_TolSym.plot.cut<-ggplot(IN_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="Retention", colour="Site")+
ylim(.25, .75)+
annotate("text", x=1.5, y=0.7, label="-", size=sig.sz, fontface="bold")
##Chlorophyll
IN_Chl.plot.cut<-ggplot(IN_Chl.sum, aes(x=Site.Treat, y=Chl_ug.cm2, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
scale_shape_manual(values=c(1,16))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="Site and Treatment", y=expression(paste('Chlorophyll (\u03BCg cm'^-2*")")), colour="Site")+
ylim(0, 1.25)
IN_TolChl.plot.cut<-ggplot(IN_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site)) +
scale_colour_manual(values=Site.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="Site", y="Retention", colour="Site")+
ylim(0, 0.25)Figure S4
##Create Panel
IN_HeatAssay_fig<-plot_grid(IN_PAM.plot.cut, IN_TolPAM.plot.cut,
IN_Sym.plot.cut, IN_TolSym.plot.cut,
IN_Chl.plot.cut, IN_TolChl.plot.cut,
rel_widths=c(1, 0.75),
rel_heights = c(1, 1, 1),
nrow=3, ncol=2, byrow=T, labels = c("A", "B", "C", "D", "E", "F"))
##Save Figure
ggsave(filename="Figures/03_Performance/FigS4_InitialHeatAssay.png", plot=IN_HeatAssay_fig, dpi=300, width=8, height=11, units="in")
Figure S5 Univariate Panels
Adjust Figures for Panel
##FvFm
TP1_TolPAM.plot.cut<-ggplot(TP1_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
ggtitle("TP1")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="Photo. Effic. Retention", colour="Origin")+
ylim(0.7, 1)+
annotate("text", x=1, y=1, label="**", size=sig.sz, fontface="bold")
TP2_TolPAM.plot.cut<-ggplot(TP2_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
ggtitle("TP2")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(0.7, 1)+
annotate("text", x=1, y=1, label="*", size=sig.sz, fontface="bold")
TP3_TolPAM.plot.cut<-ggplot(TP3_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
ggtitle("TP3")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(0.7, 1)
TP4_TolPAM.plot.cut<-ggplot(TP4_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
ggtitle("TP4")+
theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="", y="", colour="Origin")+
ylim(0.7, 1)
##Symbionts
TP1_TolSym.plot.cut<-ggplot(TP1_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="Symbiont Retention", colour="Origin")+
ylim(0.2, 1)+
annotate("text", x=1, y=0.75, label="*", size=sig.sz, fontface="bold")
TP2_TolSym.plot.cut<-ggplot(TP2_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(0.2, 1)
TP3_TolSym.plot.cut<-ggplot(TP3_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="", y="", colour="Origin")+
ylim(0.2, 1)
TP4_TolSym.plot.cut<-ggplot(TP4_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="", y="", colour="Origin")+
ylim(0.2, 1)
##Chlorophyll
TP1_TolChl.plot.cut<-ggplot(TP1_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
ylim(0, .8)
TP2_TolChl.plot.cut<-ggplot(TP2_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="Site and Origin", y="", colour="Origin")+
ylim(0, .8)
TP3_TolChl.plot.cut<-ggplot(TP3_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.position="none")+
labs(x="Site and Origin", y="", colour="Origin")+
ylim(0, .8)
TP4_TolChl.plot.cut<-ggplot(TP4_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) +
scale_colour_manual(values=Orig.colors.o)+
geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
geom_point(size=point.sz, position=position_dodge(width=0.5))+
theme_classic()+
theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz),
axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"),
legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz),
legend.box.background = element_rect(color = "black"))+
labs(x="Site and Origin", y="", colour="Origin")+
ylim(0, .8)
Figure S5
##Create Panel
Tol_fig<-plot_grid(
TP1_TolPAM.plot.cut,TP2_TolPAM.plot.cut,TP3_TolPAM.plot.cut,TP4_TolPAM.plot.cut,
TP1_TolSym.plot.cut,TP2_TolSym.plot.cut,TP3_TolSym.plot.cut,TP4_TolSym.plot.cut,
TP1_TolChl.plot.cut,TP2_TolChl.plot.cut,TP3_TolChl.plot.cut,TP4_TolChl.plot.cut,
rel_widths=c(rep(c(0.7, 0.7, 0.7,1),3)),
rel_heights = c(1,0.85,0.85),
nrow=3, ncol=4, byrow=T, labels = NULL)
##Save Figure
ggsave(filename="Figures/03_Performance/FigS5_ToleranceMetrics.png", plot=Tol_fig, dpi=300, width=12, height=10, units="in")
Tables
Table S5
##Dataframe of Performance Results
##Model Results
TableS5<-data.frame(rbind(
summary(TLE.lme)$coefficients[-1,],summary(TLE.lme_KL)$coefficients[-1,],
summary(TLE.lme_SS)$coefficients[-1,],
summary(Tol_PAM.lme_TP1)$coefficients[-1,],summary(Tol_PAM.lme_TP1_KL)$coefficients[-1,],
summary(Tol_PAM.lme_TP1_SS)$coefficients[-1,],
summary(Tol_Sym.lme_TP1)$coefficients[-1,],summary(Tol_Sym.lme_TP1_KL)$coefficients[-1,],
summary(Tol_Sym.lme_TP1_SS)$coefficients[-1,],
summary(Tol_Chl.lme_TP1)$coefficients[-1,],summary(Tol_Chl.lme_TP1_KL)$coefficients[-1,],
summary(Tol_Chl.lme_TP1_SS)$coefficients[-1,],
summary(Tol_PAM.lme_TP2)$coefficients[-1,],summary(Tol_PAM.lme_TP2_KL)$coefficients[-1,],
summary(Tol_PAM.lme_TP2_SS)$coefficients[-1,],
summary(Tol_Sym.lme_TP2)$coefficients[-1,],summary(Tol_Sym.lme_TP2_KL)$coefficients[-1,],
summary(Tol_Sym.lme_TP2_SS)$coefficients[-1,],
summary(Tol_Chl.lme_TP2)$coefficients[-1,],summary(Tol_Chl.lme_TP2_KL)$coefficients[-1,],
summary(Tol_Chl.lme_TP2_SS)$coefficients[-1,],
summary(Tol_PAM.lme_TP3)$coefficients[-1,],summary(Tol_PAM.lme_TP3_KL)$coefficients[-1,],
summary(Tol_PAM.lme_TP3_SS)$coefficients[-1,],
summary(Tol_Sym.lme_TP3)$coefficients[-1,],summary(Tol_Sym.lme_TP3_KL)$coefficients[-1,],
summary(Tol_Sym.lme_TP3_SS)$coefficients[-1,],
summary(Tol_Chl.lme_TP3)$coefficients[-1,],summary(Tol_Chl.lme_TP3_KL)$coefficients[-1,],
summary(Tol_Chl.lme_TP3_SS)$coefficients[-1,],
summary(Tol_PAM.lme_TP4)$coefficients[-1,],summary(Tol_PAM.lme_TP4_KL)$coefficients[-1,],
summary(Tol_PAM.lme_TP4_SS)$coefficients[-1,],
summary(Tol_Sym.lme_TP4)$coefficients[-1,],summary(Tol_Sym.lme_TP4_KL)$coefficients[-1,],
summary(Tol_Sym.lme_TP4_SS)$coefficients[-1,],
summary(Tol_Chl.lme_TP4)$coefficients[-1,],summary(Tol_Chl.lme_TP4_KL)$coefficients[-1,],
summary(Tol_Chl.lme_TP4_SS)$coefficients[-1,]))
names(TableS5)<-c("Estimate", "SE", "DF", "t", "p")
##Metadata
TableS5$Type<-c(rep("Growth", 5), rep("Tolerance", 60))
TableS5$Metric<-c(rep("Growth", 5), rep(c(rep("FvFm", 5), rep("Symbionts", 5), rep("Chlorophyll", 5)), 4))
TableS5$TimeP<-c(rep("TP1.2", 5), rep("TP1",15), rep("TP2",15), rep("TP3",15), rep("TP4",15))
TableS5$Predictor<-c(rep(c("Origin", "Site", "Origin x Site", "KL Origin", "SS Origin"), 13))
##Effect Size
TableS5$EtaSq<-c(
eta_squared(TLE.lme)$Eta2_partial,eta_squared(TLE.lme_KL)$Eta2_partial,
eta_squared(TLE.lme_SS)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP1)$Eta2_partial,eta_squared(Tol_PAM.lme_TP1_KL)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP1_SS)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP1)$Eta2_partial,eta_squared(Tol_Sym.lme_TP1_KL)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP1_SS)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP1)$Eta2_partial,eta_squared(Tol_Chl.lme_TP1_KL)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP1_SS)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP2)$Eta2_partial,eta_squared(Tol_PAM.lme_TP2_KL)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP2_SS)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP2)$Eta2_partial,eta_squared(Tol_Sym.lme_TP2_KL)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP2_SS)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP2)$Eta2_partial,eta_squared(Tol_Chl.lme_TP2_KL)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP2_SS)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP3)$Eta2_partial,eta_squared(Tol_PAM.lme_TP3_KL)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP3_SS)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP3)$Eta2_partial,eta_squared(Tol_Sym.lme_TP3_KL)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP3_SS)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP3)$Eta2_partial,eta_squared(Tol_Chl.lme_TP3_KL)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP3_SS)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP4)$Eta2_partial,eta_squared(Tol_PAM.lme_TP4_KL)$Eta2_partial,
eta_squared(Tol_PAM.lme_TP4_SS)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP4)$Eta2_partial,eta_squared(Tol_Sym.lme_TP4_KL)$Eta2_partial,
eta_squared(Tol_Sym.lme_TP4_SS)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP4)$Eta2_partial,eta_squared(Tol_Chl.lme_TP4_KL)$Eta2_partial,
eta_squared(Tol_Chl.lme_TP4_SS)$Eta2_partial)
TableS5<-TableS5[,c(6:9, 1:5, 10)]
rownames(TableS5)<-NULL
##Save Table
write.csv(TableS5, "Outputs/TableS5.csv", row.names=FALSE)
Table S6
##Dataframe of Initial Thermal Tolerance Assay Results
##Model Results
TableS6.model<-data.frame(rbind(
summary(PAM.lme_IN)$coefficients[-1,], summary(Sym.lme_IN)$coefficients[-1,], summary(Chl.lme_IN)$coefficients[-1,]))
names(TableS6.model)<-c("Estimate", "SE", "DF", "t", "p")
TableS6.emmeans<-data.frame(rbind(
data.frame(rbind(emmeans(PAM.lme_IN, pairwise ~ Site | Treatment)$contrasts[1], emmeans(PAM.lme_IN, pairwise ~ Site | Treatment)$contrasts[2], emmeans(PAM.lme_IN, pairwise ~ Treatment | Site)$contrasts[1], emmeans(PAM.lme_IN, pairwise ~ Treatment | Site)$contrasts[2]))[,-c(1:3)],
data.frame(rbind(emmeans(Sym.lme_IN, pairwise ~ Site | Treatment)$contrasts[1], emmeans(Sym.lme_IN, pairwise ~ Site | Treatment)$contrasts[2], emmeans(Sym.lme_IN, pairwise ~ Treatment | Site)$contrasts[1], emmeans(Sym.lme_IN, pairwise ~ Treatment | Site)$contrasts[2]))[,-c(1:3)],
data.frame(rbind(emmeans(Chl.lme_IN, pairwise ~ Site | Treatment)$contrasts[1], emmeans(Chl.lme_IN, pairwise ~ Site | Treatment)$contrasts[2], emmeans(Chl.lme_IN, pairwise ~ Treatment | Site)$contrasts[1], emmeans(Chl.lme_IN, pairwise ~ Treatment | Site)$contrasts[2]))[,-c(1:3)]))
names(TableS6.emmeans)<-c("Estimate", "SE", "DF", "t", "p")
TableS6<-rbind(TableS6.model[1:3,], TableS6.emmeans[1:4,], TableS6.model[4:6,], TableS6.emmeans[5:8,], TableS6.model[7:9,], TableS6.emmeans[9:12,])
##Metadata
TableS6$Metric<-c(rep("FvFm", 7), rep("Symbionts", 7), rep("Chlorophyll", 7))
TableS6$Predictor<-c(rep(c("Site", "Treatment", "Site x Treatment", "Control Site", "Heated Site", "KL Treatment", "SS Treatment"), 3))
##Effect Size
TableS6$EtaSq<-c(eta_squared(PAM.lme_IN)$Eta2_partial, NA, NA, NA, NA, eta_squared(Sym.lme_IN)$Eta2_partial, NA, NA, NA, NA, eta_squared(Chl.lme_IN)$Eta2_partial, NA, NA, NA, NA)
TableS6<-TableS6[,c(6:7, 1:5, 8)]
rownames(TableS6)<-NULL
##Save Table
write.csv(TableS6, "Outputs/TableS6.csv", row.names=FALSE)
---
title: "Coral Performance Following Reciprocal Transplant"
author: "Serena Hackerott and Carly Travers"
date: "1/3/2024"
output:
  html_notebook:
    toc: yes
    toc_float: yes
  html_document:
    toc: yes
    df_print: paged
---

# Setup

```{r Setup, include = FALSE}
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)
```


### Load Packages
```{r}
##Install Packages if Needed
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("cowplot")) install.packages("cowplot")
if (!require("lubridate")) install.packages("lubridate")
if (!require("corrplot")) install.packages("corrplot")
if (!require("lme4")) install.packages("lme4")
if (!require("vegan")) install.packages("vegan")
if (!require("DHARMa")) install.packages("DHARMa")
if (!require("effectsize")) install.packages("effectsize")
if (!require("emmeans")) install.packages("emmeans")
if (!require("lmerTest")) install.packages("lmerTest")
if (!require("tidyr")) install.packages("tidyr")
if (!require("dplyr")) install.packages("dplyr")
if (!require("plotrix")) install.packages("plotrix")
if (!require("Rmisc")) install.packages("Rmisc")
if (!require("ggpubr")) install.packages("ggpubr")

##Load Packages
library(ggplot2) #Required for ggplots
library(cowplot) #Required for plotting panel figures
library(lubridate) #Required for date and time formatting
library(corrplot) #Required for correlation plot
library(lme4) #Required for mixed effects modeling
library(vegan) #Required for multivariate analysis PERM
library(DHARMa) #Required to check residuals of mixed effects models
library(effectsize) #Required for eta_squared effect sizes
library(emmeans) #Required for pairwise comparisons 
library(lmerTest) #Required for p values with lme4 model summaries
library(tidyr) #Required for pivot_wider function
library(dplyr) #Required for group_by function
library(plotrix) #Required for Standard error function
library(Rmisc) #Required for SummarySE function
library(ggpubr) #Required for stat brackets

```


### Graphing Parameters
```{r}
#Note: Run "Graphing Parameters" section from 01_ExperimentalSetup.Rmd file
```


# Sample Data and Metadata

### Load and Organize Data
```{r}
##Growth
Length<-read.csv("Data/Length.csv", header=TRUE)
Dates<-read.csv("Data/BonaireDates.csv", header=TRUE)

##Set date variables
Dates$Date<-as.Date(Dates$Date, format='%m/%d/%Y')

##Set factor variables
Length$Site<-factor(Length$Site, levels=c("KL", "SS"))
Length$Genotype<-factor(Length$Genotype, levels=c("AC8", "AC10", "AC12"))
Length$Orig<-factor(Length$Orig, levels=c("N", "T"))
Length$Origin<-factor(Length$Origin, levels=c("Native", "Transplant"))

##Add a Sample Set Variable
Length$Set<-paste(Length$Site, Length$Genotype, Length$Orig, sep=".")
Length$Set<-factor(Length$Set)

##Add Site.Orig variable
Length$Site.Orig<-paste(Length$Site, Length$Orig, sep=".")
Length$Site.Orig<-factor(Length$Site.Orig, levels=c("KL.N", "KL.T", "SS.N", "SS.T"))


##Bleaching Metrics
#Note: Physiological metrics calculated in 02_PhysiologyMetrics.R file
Thermal<-read.csv("Outputs/ThermData.csv", header=TRUE)

##Set factor variables
Thermal$TimeP<-factor(Thermal$TimeP, levels=c("IN", "TP1", "TP2", "TP3", "TP4"))
Thermal$Site<-factor(Thermal$Site, levels=c("KL", "SS"))
Thermal$Genotype<-factor(Thermal$Genotype, levels=c("AC8", "AC10", "AC12"))
Thermal$Orig<-factor(Thermal$Orig, levels=c("N", "T"))
Thermal$Origin<-factor(Thermal$Origin, levels=c("Native", "Transplant"))

```


# Growth

### Growth Rate TP1 to TP2

#### Calculate Days
```{r}
##Subset Dates for TP 1 and TP 2 Growth
Growth.Dates<-subset(Dates, Task=="Growth" & TimeP=="TP1" |
                       Task=="Growth" & TimeP=="TP2" )

##Convert to Wide format
Growth.Dates<-Growth.Dates %>%  pivot_wider(names_from = TimeP, values_from = Date)

##Calculate Number of Days
#TP 1 to TP2
Growth.Dates$TP1.2_days<-time_length(Growth.Dates$TP2-Growth.Dates$TP1, unit="days")

```


#### Calculate Total Linear Extension
```{r}
##Merge Length Data with Growth Dates
#Merges by Site column
#Adds TP1.2_days column
Growth<-merge(Length, Growth.Dates[,c(-2)], all.x=TRUE)

##Calculate Linear Extension (cm)
Growth$Ext_cm<-Growth$TL.TP2_cm-Growth$TL.TP1_cm

##Calculate Growth Rate (cm/day)
Growth$TLE_cm.day<-Growth$Ext_cm/Growth$TP1.2_days

```


#### Data QC
```{r}
##Sample Size per Set
Growth %>% 
  dplyr::group_by(Set) %>%
  dplyr::summarize(SampleSize = length(Set))

##Check for Outliers
ggplot(Growth, aes(x=Set, y=TLE_cm.day)) + 
  geom_boxplot(alpha=0.5, shape=2, outlier.shape = NA)+
  geom_jitter(shape=16, position=position_jitter(0.1))+
  theme(axis.text.x = element_text(angle = 90))

```

Potential outliers have been re-measured and therefore will be retained. n=10 per Site, Genotype, Origin group


### Compare Growth Rate
```{r}
#Check normality
hist(Growth$TLE_cm.day)
shapiro.test(Growth$TLE_cm.day)
#Not normal

hist(log(Growth$TLE_cm.day+1))
shapiro.test(log(Growth$TLE_cm.day+1))
##Still not normal

##Compare generalized linear mixed effects models
TLE.glmr.gaus<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=gaussian(link="identity"))
TLE.glmr.gaus.l<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=gaussian(link="log"))
TLE.glmr.gaus.i<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=gaussian(link="inverse"))

TLE.glmr.gam.l<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=Gamma(link="log"))
TLE.glmr.gam.i<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=Gamma(link="inverse"))

TLE.glmr.inv<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=inverse.gaussian(link="1/mu^2"))
TLE.glmr.inv.l<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=inverse.gaussian(link="log"))
TLE.glmr.inv.i<-glmer(TLE_cm.day~Origin*Site+(1|Genotype), data=Growth, family=inverse.gaussian(link="inverse"))

AIC(TLE.glmr.gaus, TLE.glmr.gaus.l, TLE.glmr.gaus.i, TLE.glmr.gam.l, TLE.glmr.gam.i, TLE.glmr.inv, TLE.glmr.inv.l, TLE.glmr.inv.i)
```

Gamma distribution with log-link has the lowest AIC.


```{r}
##Check residuals
TLE.glmr.gam.l_res <- simulateResiduals(fittedModel = TLE.glmr.gam.l, plot = F)
plot(TLE.glmr.gam.l_res)

```


Compare residuals across models

```{r}
##Check residuals
TLE.glmr.gaus.l_res <- simulateResiduals(fittedModel = TLE.glmr.gaus.l, plot = F)
plot(TLE.glmr.gaus.l_res)

```

Compare with log+1 transformed lmer model

```{r}
##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
TLE.lme<-lmer(log(TLE_cm.day+1)~Origin*Site+(1|Genotype), data=Growth)

##Check residuals
TLE.lme_res <- simulateResiduals(fittedModel = TLE.lme, plot = F)
plot(TLE.lme_res)
```
Better residuals with the log+1 transformed lmer model.


```{r}
##Model results
summary(TLE.lme)

eta_squared(TLE.lme)
```


Effect size of Origin for each Site
```{r}
##KL
TLE.lme_KL<-lmer(log(TLE_cm.day+1)~Origin+(1|Genotype), data=Growth[which(Growth$Site=="KL"),])
summary(TLE.lme_KL)
eta_squared(TLE.lme_KL)


##SS
TLE.lme_SS<-lmer(log(TLE_cm.day+1)~Origin+(1|Genotype), data=Growth[which(Growth$Site=="SS"),])
summary(TLE.lme_SS)
eta_squared(TLE.lme_SS)

```


```{r Plot Growth Rate TP1 to 2}
##Summary statistics by Site and Origin
TP1.2_Growth.sum<-summarySE(Growth, measurevar="TLE_cm.day", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Growth Rate across Treatments
TP1.2_Growth.plot<-ggplot(TP1.2_Growth.sum, aes(x=Site, y=TLE_cm.day, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TLE_cm.day-se, ymax=TLE_cm.day+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y=expression(paste('Growth Rate (cm day'^-1*")")), colour="Origin")+
  ylim(0, 0.25)+
  annotate("text", x=2, y=0.24, label="*", size=sig.sz, fontface="bold")+
  geom_bracket(xmin=1, xmax=2, y.position=0.01, label.size=levels.sz, label="**", inherit.aes = FALSE); TP1.2_Growth.plot
```


### Average Growth Rates by Genotype
```{r}
##Summary statistics by Genotype and Origin
TP1.2_Growth.sum.SS<-summarySE(subset(Growth, Site=="SS"), measurevar="TLE_cm.day", groupvars=c("Genotype", "Origin"), na.rm=TRUE)

TP1.2_Growth.sum.SS

##Calculate Percent Difference between Native and Transplant by Genotype
TP1.2_Growth.SS<-TP1.2_Growth.sum.SS[which(TP1.2_Growth.sum.SS$Origin=="Native"), c(1,4)]
names(TP1.2_Growth.SS)[2]<-paste(names(TP1.2_Growth.SS)[2], "N", sep="_")
TP1.2_Growth.SS$TLE_cm.day_T<-TP1.2_Growth.sum.SS$TLE_cm.day[which(TP1.2_Growth.sum.SS$Origin=="Transplant")]
TP1.2_Growth.SS$Perc.Inc<-((TP1.2_Growth.SS$TLE_cm.day_N-TP1.2_Growth.SS$TLE_cm.day_T)/TP1.2_Growth.SS$TLE_cm.day_T)*100

TP1.2_Growth.SS

##Mean and Standard Error
mean(TP1.2_Growth.SS$Perc.Inc)
std.error(TP1.2_Growth.SS$Perc.Inc)
```


# Thermal Tolerance

### Percent Retention

#### Subset Thermal Assay Data by Treatment
```{r}
##Control
Therm_C<-subset(Thermal, Treat=="C")

##Heated
Therm_H<-subset(Thermal, Treat=="H")
```


#### Calculate Percent Retention
Calculating retention as proportion remaining relative to corresponding control levels (0-1) for each site and genotype at each timepoint.
```{r}
##Calculate averages for Control Treatment for each Site, Genotype, and Timepoint 
Therm_C<-na.omit(Therm_C)
names(Therm_C)
Therm_C.a<-aggregate(Therm_C[,c(13:15)], list(Therm_C$Site, Therm_C$Genotype, Therm_C$TimeP, Therm_C$Origin), mean, na.action = na.omit)
names(Therm_C.a)[1:4]<-c("Site", "Genotype", "TimeP", "Origin")
names(Therm_C.a)[5:7]<-paste(names(Therm_C.a)[5:7], "C", sep="_")

##Merge Control Averages with Heated Samples
#Merges by Site, Genotype, and Timepoint
Therm_H<-merge(Therm_H, Therm_C.a, all.x=TRUE )

##Calculate Proportion Retained for each Bleaching Metric relative to Control
Therm_H$Chl.prop<-round((Therm_H$Chl_ug.cm2/Therm_H$Chl_ug.cm2_C), 4)
Therm_H$Sym.prop<-round((Therm_H$Sym10.6_cm2/Therm_H$Sym10.6_cm2_C), 4)
Therm_H$PAM.prop<-round((Therm_H$Fv_Fm/Therm_H$Fv_Fm_C), 4)

##Set values >1 to 1
Therm_H$Chl.prop[which(Therm_H$Chl.prop>1)]<-1.0000
Therm_H$Sym.prop[which(Therm_H$Sym.prop>1)]<-1.0000
Therm_H$PAM.prop[which(Therm_H$PAM.prop>1)]<-1.0000

```


# Initial Heat Assay 

```{r}
##Subset Initial Timepoint
Thermal_IN<-subset(Thermal, TimeP=="IN")
Thermal_IN$Site.Treat<-paste(Thermal_IN$Site, Thermal_IN$Treat, sep=".")
Thermal_IN$Site.Treat<-factor(Thermal_IN$Site.Treat, levels=c("KL.C", "KL.H", "SS.C", "SS.H"))


Therm_IN<-subset(Therm_H, TimeP=="IN")
```


### Heated vs Control


#### Symbionts
```{r}
#Check normality
hist(Thermal_IN$Sym10.6_cm2)
shapiro.test(Thermal_IN$Sym10.6_cm2)
#Not normal

hist(log(Thermal_IN$Sym10.6_cm2+1))
shapiro.test(log(Thermal_IN$Sym10.6_cm2+1))
#Normal

##Model
#Function of Site and Treatment, with Genotype as a Random effect
Sym.lme_IN<-lmer(log(Sym10.6_cm2+1)~Site*Treatment+(1|Genotype), data=Thermal_IN)

##Check residuals
Sym.lme_res_IN <- simulateResiduals(fittedModel = Sym.lme_IN, plot = F)
plot(Sym.lme_res_IN)

##Model results
summary(Sym.lme_IN)

eta_squared(Sym.lme_IN)

emmeans(Sym.lme_IN, pairwise ~ Site | Treatment)

emmeans(Sym.lme_IN, pairwise ~ Treatment | Site)

```


```{r Plot Symbionts Initial}
##Summary statistics by Site
IN_Sym.sum<-summarySE(Thermal_IN, measurevar="Sym10.6_cm2", groupvars=c("Site", "Treatment", "Site.Treat"), na.rm=TRUE)

##Plot Average Symbionts across Treatments
IN_Sym.plot<-ggplot(IN_Sym.sum, aes(x=Site.Treat, y=Sym10.6_cm2, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Sym10.6_cm2-se, ymax=Sym10.6_cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
  scale_shape_manual(values=c(1,16))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Treatment", y=expression(paste('Symbiont Density ('*10^6,'cells cm'^-2*")")), colour="Site")+
  ylim(0, 1); IN_Sym.plot
```


#### Chlorophyll
```{r}
#Check normality
hist(Thermal_IN$Chl_ug.cm2)
shapiro.test(Thermal_IN$Chl_ug.cm2)
#Not normal

hist(log(Thermal_IN$Chl_ug.cm2+1))
shapiro.test(log(Thermal_IN$Chl_ug.cm2+1))
#Still not normal but less skewed

##Model
#Function of Site and Treatment, with Genotype as a Random effect
Chl.lme_IN<-lmer(log(Chl_ug.cm2+1)~Site*Treatment+(1|Genotype), data=Thermal_IN)

##Check residuals
Chl.lme_res_IN <- simulateResiduals(fittedModel = Chl.lme_IN, plot = F)
plot(Chl.lme_res_IN)

##Model results
summary(Chl.lme_IN)

eta_squared(Chl.lme_IN)

emmeans(Chl.lme_IN, pairwise ~ Site | Treatment)

emmeans(Chl.lme_IN, pairwise ~ Treatment | Site)

```


```{r Plot Chlorophyll Initial}
##Summary statistics by Site
IN_Chl.sum<-summarySE(Thermal_IN, measurevar="Chl_ug.cm2", groupvars=c("Site", "Treatment", "Site.Treat"), na.rm=TRUE)

##Plot Average Chlorophyll across Treatments
IN_Chl.plot<-ggplot(IN_Chl.sum, aes(x=Site.Treat, y=Chl_ug.cm2, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
  scale_shape_manual(values=c(1,16))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Treatment", y=expression(paste('Total Chlorophyll (\u03BCg cm'^-2*")")), colour="Site")+
  ylim(0, 1.25); IN_Chl.plot
```


#### FvFm
```{r}
#Check normality
hist(Thermal_IN$Fv_Fm)
shapiro.test(Thermal_IN$Fv_Fm)
#Not normal

hist(log(Thermal_IN$Fv_Fm+1))
shapiro.test(log(Thermal_IN$Fv_Fm+1))
#Still not normal but less skewed

##Model
#Function of Site and Treatment, with Genotype as a Random effect
PAM.lme_IN<-lmer(log(Fv_Fm+1)~Site*Treatment+(1|Genotype), data=Thermal_IN)

##Check residuals
PAM.lme_res_IN <- simulateResiduals(fittedModel = PAM.lme_IN, plot = F)
plot(PAM.lme_res_IN)

##Model results
summary(PAM.lme_IN)

eta_squared(PAM.lme_IN)

emmeans(PAM.lme_IN, pairwise ~ Site | Treatment)

emmeans(PAM.lme_IN, pairwise ~ Treatment | Site)

```
Not normal, but model residuals are OK.


```{r Plot FvFm Initial}
##Summary statistics by Site
IN_PAM.sum<-summarySE(Thermal_IN, measurevar="Fv_Fm", groupvars=c("Site", "Treatment", "Site.Treat"), na.rm=TRUE)

##Plot Average FvFm across Treatments
IN_PAM.plot<-ggplot(IN_PAM.sum, aes(x=Site.Treat, y=Fv_Fm, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Fv_Fm-se, ymax=Fv_Fm+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
  scale_shape_manual(values=c(1,16))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Treatment", y="Photochemical Efficiency (Fv/Fm)", colour="Site")+
  ylim(0.5, 0.655); IN_PAM.plot
```


### Retention by Site

#### Symbionts
```{r}
#Check normality
hist(Therm_IN$Sym.prop)
shapiro.test(Therm_IN$Sym.prop)
#Normal

##Model
#Function of Site, with Genotype as a Random effect
Tol_Sym.lme_IN<-lmer(Sym.prop~Site+(1|Genotype), data=Therm_IN)

##Check residuals
Tol_Sym.lme_res_IN <- simulateResiduals(fittedModel = Tol_Sym.lme_IN, plot = F)
plot(Tol_Sym.lme_res_IN)

##Model results
summary(Tol_Sym.lme_IN)

eta_squared(Tol_Sym.lme_IN)

```


```{r Plot Symbiont Retention Initial}
##Summary statistics by Site
IN_TolSym.sum<-summarySE(Therm_IN, measurevar="Sym.prop", groupvars=c("Site"), na.rm=TRUE)

##Plot Average Symbiont Retention across Treatments
IN_TolSym.plot<-ggplot(IN_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site", y="Symbiont Retention", colour="Site")+
  ylim(0, 1)+
  annotate("text", x=1.5, y=0.75, label="-", size=sig.sz, fontface="bold"); IN_TolSym.plot
```


#### Chlorophyll
```{r}
#Check normality
hist(Therm_IN$Chl.prop)
shapiro.test(Therm_IN$Chl.prop)
#Normal

##Model
#Function of Site, with Genotype as a Random effect
Tol_Chl.lme_IN<-lmer(Chl.prop~Site+(1|Genotype), data=Therm_IN)

##Check residuals
Tol_Chl.lme_res_IN <- simulateResiduals(fittedModel = Tol_Chl.lme_IN, plot = F)
plot(Tol_Chl.lme_res_IN)

##Model results
summary(Tol_Chl.lme_IN)

eta_squared(Tol_Chl.lme_IN)

```


```{r Plot Chlorophyll Retention Initial}
##Summary statistics by Site and Origin
IN_TolChl.sum<-summarySE(Therm_IN, measurevar="Chl.prop", groupvars=c("Site"), na.rm=TRUE)

##Plot Average Chlorophyll Retention across Treatments
IN_TolChl.plot<-ggplot(IN_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site", y="Chlorophyll Retention", colour="Site")+
  ylim(0, 0.35); IN_TolChl.plot
```


#### FvFm
```{r}
#Check normality
hist(Therm_IN$PAM.prop)
shapiro.test(Therm_IN$PAM.prop)
#Not normal

hist(log(Therm_IN$PAM.prop+1))
shapiro.test(log(Therm_IN$PAM.prop+1))
##Still not normal

##Model
#Function of Site, with Genotype as a Random effect
Tol_PAM.lme_IN<-lmer(PAM.prop~Site+(1|Genotype), data=Therm_IN)

##Check residuals
Tol_PAM.lme_res_IN <- simulateResiduals(fittedModel = Tol_PAM.lme_IN, plot = F)
plot(Tol_PAM.lme_res_IN)

##Model results
summary(Tol_PAM.lme_IN)

eta_squared(Tol_PAM.lme_IN)

```
Not normal, but model residuals are OK.


```{r Plot FvFm Retention Initial}
##Summary statistics by Site and Origin
IN_TolPAM.sum<-summarySE(Therm_IN, measurevar="PAM.prop", groupvars=c("Site"), na.rm=TRUE)

##Plot Average FvFm Retention across Treatments
IN_TolPAM.plot<-ggplot(IN_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site", y="Photochemical Efficiency Retention", colour="Site")+
  ylim(0.6, 1)+
  annotate("text", x=1.5, y=1, label="*", size=sig.sz, fontface="bold"); IN_TolPAM.plot
```


# Tolerance after Transplant


### TP1
```{r}
##Subset Timepoint 1
Therm_TP1<-subset(Therm_H, TimeP=="TP1")
```


#### Symbionts
```{r}
#Check normality
hist(Therm_TP1$Sym.prop)
shapiro.test(Therm_TP1$Sym.prop)
#Not normal

hist(log(Therm_TP1$Sym.prop+1))
shapiro.test(log(Therm_TP1$Sym.prop+1))
##Still not normal but less skewed

##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Sym.lme_TP1<-lmer(log(Sym.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP1)

##Check residuals
Tol_Sym.lme_res_TP1 <- simulateResiduals(fittedModel = Tol_Sym.lme_TP1, plot = F)
plot(Tol_Sym.lme_res_TP1)

##Model results
summary(Tol_Sym.lme_TP1)

eta_squared(Tol_Sym.lme_TP1)

```
Not normal, but model residuals are OK.


Effect size of Origin for each Site
```{r}
##KL
Tol_Sym.lme_TP1_KL<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="KL"),])
summary(Tol_Sym.lme_TP1_KL)
eta_squared(Tol_Sym.lme_TP1_KL)


##SS
Tol_Sym.lme_TP1_SS<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="SS"),])
summary(Tol_Sym.lme_TP1_SS)
eta_squared(Tol_Sym.lme_TP1_SS)

```


```{r Plot Symbiont Retention TP1}
##Summary statistics by Site and Origin
TP1_TolSym.sum<-summarySE(Therm_TP1, measurevar="Sym.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Symbiont Retention across Treatments
TP1_TolSym.plot<-ggplot(TP1_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Symbiont Retention", colour="Origin")+
  ylim(0, 1)+
  annotate("text", x=1, y=0.75, label="*", size=sig.sz, fontface="bold"); TP1_TolSym.plot
```


#### Chlorophyll
```{r}
#Check normality
hist(Therm_TP1$Chl.prop)
shapiro.test(Therm_TP1$Chl.prop)
#Not normal

hist(log(Therm_TP1$Chl.prop+1))
shapiro.test(log(Therm_TP1$Chl.prop+1))
##Still not normal but less skewed

##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Chl.lme_TP1<-lmer(log(Chl.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP1)

##Check residuals
Tol_Chl.lme_res_TP1 <- simulateResiduals(fittedModel = Tol_Chl.lme_TP1, plot = F)
plot(Tol_Chl.lme_res_TP1)

##Model results
summary(Tol_Chl.lme_TP1)

eta_squared(Tol_Chl.lme_TP1)

```


Effect size of Origin for each Site
```{r}
##KL
Tol_Chl.lme_TP1_KL<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="KL"),])
summary(Tol_Chl.lme_TP1_KL)
eta_squared(Tol_Chl.lme_TP1_KL)


##SS
Tol_Chl.lme_TP1_SS<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="SS"),])
summary(Tol_Chl.lme_TP1_SS)
eta_squared(Tol_Chl.lme_TP1_SS)

```


```{r Plot Chlorophyll Retention TP1}
##Summary statistics by Site and Origin
TP1_TolChl.sum<-summarySE(Therm_TP1, measurevar="Chl.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Chlorophyll Retention across Treatments
TP1_TolChl.plot<-ggplot(TP1_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
  ylim(0, 0.35); TP1_TolChl.plot
```


#### FvFm
```{r}
#Check normality
hist(Therm_TP1$PAM.prop)
shapiro.test(Therm_TP1$PAM.prop)
#Not normal

hist(log(Therm_TP1$PAM.prop+1))
shapiro.test(log(Therm_TP1$PAM.prop+1))
##Still not normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_PAM.lme_TP1<-lmer(PAM.prop~Origin*Site+(1|Genotype), data=Therm_TP1)

##Check residuals
Tol_PAM.lme_res_TP1 <- simulateResiduals(fittedModel = Tol_PAM.lme_TP1, plot = F)
plot(Tol_PAM.lme_res_TP1)

##Model results
summary(Tol_PAM.lme_TP1)

eta_squared(Tol_PAM.lme_TP1)

```
Not normal, but model residuals are OK.


Effect size of Origin for each Site
```{r}
##KL
Tol_PAM.lme_TP1_KL<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="KL"),])
summary(Tol_PAM.lme_TP1_KL)
eta_squared(Tol_PAM.lme_TP1_KL)


##SS
Tol_PAM.lme_TP1_SS<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP1[which(Therm_TP1$Site=="SS"),])
summary(Tol_PAM.lme_TP1_SS)
eta_squared(Tol_PAM.lme_TP1_SS)

```


```{r Plot FvFm Retention TP1}
##Summary statistics by Site and Origin
TP1_TolPAM.sum<-summarySE(Therm_TP1, measurevar="PAM.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average FvFm Retention across Treatments
TP1_TolPAM.plot<-ggplot(TP1_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Photochemical Efficiency Retention", colour="Origin")+
  ylim(0.6, 1)+
  annotate("text", x=1, y=1, label="**", size=sig.sz, fontface="bold"); TP1_TolPAM.plot
```


### TP2
```{r}
##Subset Timepoint 2
Therm_TP2<-subset(Therm_H, TimeP=="TP2")
```


#### Symbionts
```{r}
#Check normality
hist(Therm_TP2$Sym.prop)
shapiro.test(Therm_TP2$Sym.prop)
#Not normal

hist(log(Therm_TP2$Sym.prop+1))
shapiro.test(log(Therm_TP2$Sym.prop+1))
##Still not normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Sym.lme_TP2<-lmer(Sym.prop~Origin*Site+(1|Genotype), data=Therm_TP2)

##Check residuals
Tol_Sym.lme_res_TP2 <- simulateResiduals(fittedModel = Tol_Sym.lme_TP2, plot = F)
plot(Tol_Sym.lme_res_TP2)

##Model results
summary(Tol_Sym.lme_TP2)

eta_squared(Tol_Sym.lme_TP2)

```
Not normal, but model residuals are OK.


Effect size of Origin for each Site
```{r}
##KL
Tol_Sym.lme_TP2_KL<-lmer(Sym.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="KL"),])
summary(Tol_Sym.lme_TP2_KL)
eta_squared(Tol_Sym.lme_TP2_KL)


##SS
Tol_Sym.lme_TP2_SS<-lmer(Sym.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="SS"),])
summary(Tol_Sym.lme_TP2_SS)
eta_squared(Tol_Sym.lme_TP2_SS)

```


```{r Plot Symbiont Retention TP2}
##Summary statistics by Site and Origin
TP2_TolSym.sum<-summarySE(Therm_TP2, measurevar="Sym.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP2_TolSym.plot<-ggplot(TP2_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Symbiont Retention", colour="Origin")+
  ylim(0, 1); TP2_TolSym.plot
```


#### Chlorophyll
```{r}
#Check normality
hist(Therm_TP2$Chl.prop)
shapiro.test(Therm_TP2$Chl.prop)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Chl.lme_TP2<-lmer(Chl.prop~Origin*Site+(1|Genotype), data=Therm_TP2)

##Check residuals
Tol_Chl.lme_res_TP2 <- simulateResiduals(fittedModel = Tol_Chl.lme_TP2, plot = F)
plot(Tol_Chl.lme_res_TP2)

##Model results
summary(Tol_Chl.lme_TP2)

eta_squared(Tol_Chl.lme_TP2)

```


Effect size of Origin for each Site
```{r}
##KL
Tol_Chl.lme_TP2_KL<-lmer(Chl.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="KL"),])
summary(Tol_Chl.lme_TP2_KL)
eta_squared(Tol_Chl.lme_TP2_KL)


##SS
Tol_Chl.lme_TP2_SS<-lmer(Chl.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="SS"),])
summary(Tol_Chl.lme_TP2_SS)
eta_squared(Tol_Chl.lme_TP2_SS)

```


```{r Plot Chlorophyll Retention TP2}
##Summary statistics by Site and Origin
TP2_TolChl.sum<-summarySE(Therm_TP2, measurevar="Chl.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP2_TolChl.plot<-ggplot(TP2_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
  ylim(0, 0.35); TP2_TolChl.plot
```


#### FvFm
```{r}
#Check normality
hist(Therm_TP2$PAM.prop)
shapiro.test(Therm_TP2$PAM.prop)
#Not normal

hist(log(Therm_TP2$PAM.prop+1))
shapiro.test(log(Therm_TP2$PAM.prop+1))
##Still not normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_PAM.lme_TP2<-lmer(PAM.prop~Origin*Site+(1|Genotype), data=Therm_TP2)

##Check residuals
Tol_PAM.lme_res_TP2 <- simulateResiduals(fittedModel = Tol_PAM.lme_TP2, plot = F)
plot(Tol_PAM.lme_res_TP2)

##Model results
summary(Tol_PAM.lme_TP2)

eta_squared(Tol_PAM.lme_TP2)

```
Not normal, but model residuals are OK.


Effect size of Origin for each Site
```{r}
##KL
Tol_PAM.lme_TP2_KL<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="KL"),])
summary(Tol_PAM.lme_TP2_KL)
eta_squared(Tol_PAM.lme_TP2_KL)


##SS
Tol_PAM.lme_TP2_SS<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP2[which(Therm_TP2$Site=="SS"),])
summary(Tol_PAM.lme_TP2_SS)
eta_squared(Tol_PAM.lme_TP2_SS)

```


```{r Plot FvFm Retention TP2}
##Summary statistics by Site and Origin
TP2_TolPAM.sum<-summarySE(Therm_TP2, measurevar="PAM.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP2_TolPAM.plot<-ggplot(TP2_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Photochemical Efficiency Retention", colour="Origin")+
  ylim(0.6, 1)+
  annotate("text", x=1, y=1, label="*", size=sig.sz, fontface="bold"); TP2_TolPAM.plot
```


### TP3
```{r}
##Subset Timepoint 3
Therm_TP3<-subset(Therm_H, TimeP=="TP3")
```


#### Symbionts
```{r}
#Check normality
hist(Therm_TP3$Sym.prop)
shapiro.test(Therm_TP3$Sym.prop)
#Not normal

hist(log(Therm_TP3$Sym.prop+1))
shapiro.test(log(Therm_TP3$Sym.prop+1))
##Still not normal but less skewed

##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Sym.lme_TP3<-lmer(log(Sym.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP3)

##Check residuals
Tol_Sym.lme_res_TP3 <- simulateResiduals(fittedModel = Tol_Sym.lme_TP3, plot = F)
plot(Tol_Sym.lme_res_TP3)

##Model results
summary(Tol_Sym.lme_TP3)

eta_squared(Tol_Sym.lme_TP3)

```
Not normal, but model residuals are OK.


Effect size of Origin for each Site
```{r}
##KL
Tol_Sym.lme_TP3_KL<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="KL"),])
summary(Tol_Sym.lme_TP3_KL)
eta_squared(Tol_Sym.lme_TP3_KL)


##SS
Tol_Sym.lme_TP3_SS<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="SS"),])
summary(Tol_Sym.lme_TP3_SS)
eta_squared(Tol_Sym.lme_TP3_SS)

```


```{r Plot Symbiont Retention TP3}
##Summary statistics by Site and Origin
TP3_TolSym.sum<-summarySE(Therm_TP3, measurevar="Sym.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP3_TolSym.plot<-ggplot(TP3_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Symbiont Retention", colour="Origin")+
  ylim(0, 1); TP3_TolSym.plot
```


#### Chlorophyll
```{r}
#Check normality
hist(Therm_TP3$Chl.prop)
shapiro.test(Therm_TP3$Chl.prop)
#Not normal

hist(log(Therm_TP3$Chl.prop+1))
shapiro.test(log(Therm_TP3$Chl.prop+1))
##Still not normal but less skewed

##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Chl.lme_TP3<-lmer(log(Chl.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP3)

##Check residuals
Tol_Chl.lme_res_TP3 <- simulateResiduals(fittedModel = Tol_Chl.lme_TP3, plot = F)
plot(Tol_Chl.lme_res_TP3)

##Model results
summary(Tol_Chl.lme_TP3)

eta_squared(Tol_Chl.lme_TP3)

```
Not normal, but model residuals are OK.


Effect size of Origin for each Site
```{r}
##KL
Tol_Chl.lme_TP3_KL<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="KL"),])
summary(Tol_Chl.lme_TP3_KL)
eta_squared(Tol_Chl.lme_TP3_KL)


##SS
Tol_Chl.lme_TP3_SS<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="SS"),])
summary(Tol_Chl.lme_TP3_SS)
eta_squared(Tol_Chl.lme_TP3_SS)

```


```{r Plot Chlorophyll Retention TP3}
##Summary statistics by Site and Origin
TP3_TolChl.sum<-summarySE(Therm_TP3, measurevar="Chl.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP3_TolChl.plot<-ggplot(TP3_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
  ylim(0, 1); TP3_TolChl.plot
```


#### FvFm
```{r}
#Check normality
hist(Therm_TP3$PAM.prop)
shapiro.test(Therm_TP3$PAM.prop)
#Normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_PAM.lme_TP3<-lmer(PAM.prop~Origin*Site+(1|Genotype), data=Therm_TP3)

##Check residuals
Tol_PAM.lme_res_TP3 <- simulateResiduals(fittedModel = Tol_PAM.lme_TP3, plot = F)
plot(Tol_PAM.lme_res_TP3)

##Model results
summary(Tol_PAM.lme_TP3)

eta_squared(Tol_PAM.lme_TP3)

```


Effect size of Origin for each Site
```{r}
##KL
Tol_PAM.lme_TP3_KL<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="KL"),])
summary(Tol_PAM.lme_TP3_KL)
eta_squared(Tol_PAM.lme_TP3_KL)


##SS
Tol_PAM.lme_TP3_SS<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP3[which(Therm_TP3$Site=="SS"),])
summary(Tol_PAM.lme_TP3_SS)
eta_squared(Tol_PAM.lme_TP3_SS)

```


```{r Plot FvFm Retention TP3}
##Summary statistics by Site and Origin
TP3_TolPAM.sum<-summarySE(Therm_TP3, measurevar="PAM.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP3_TolPAM.plot<-ggplot(TP3_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Photochemical Efficiency Retention", colour="Origin")+
  ylim(0.7, 1); TP3_TolPAM.plot
```


### TP4
```{r}
##Subset Timepoint 4
Therm_TP4<-subset(Therm_H, TimeP=="TP4")
```


#### Symbionts
```{r}
#Check normality
hist(Therm_TP4$Sym.prop)
shapiro.test(Therm_TP4$Sym.prop)
#Not normal

hist(log(Therm_TP4$Sym.prop+1))
shapiro.test(log(Therm_TP4$Sym.prop+1))
#Normal

##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Sym.lme_TP4<-lmer(log(Sym.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP4)

##Check residuals
Tol_Sym.lme_res_TP4 <- simulateResiduals(fittedModel = Tol_Sym.lme_TP4, plot = F)
plot(Tol_Sym.lme_res_TP4)

##Model results
summary(Tol_Sym.lme_TP4)

eta_squared(Tol_Sym.lme_TP4)

```


Effect size of Origin for each Site
```{r}
##KL
Tol_Sym.lme_TP4_KL<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="KL"),])
summary(Tol_Sym.lme_TP4_KL)
eta_squared(Tol_Sym.lme_TP4_KL)


##SS
Tol_Sym.lme_TP4_SS<-lmer(log(Sym.prop+1)~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="SS"),])
summary(Tol_Sym.lme_TP4_SS)
eta_squared(Tol_Sym.lme_TP4_SS)

```


```{r Plot Symbiont Retention TP4}
##Summary statistics by Site and Origin
TP4_TolSym.sum<-summarySE(Therm_TP4, measurevar="Sym.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP4_TolSym.plot<-ggplot(TP4_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Symbiont Retention", colour="Origin")+
  ylim(0, 1); TP4_TolSym.plot
```


#### Chlorophyll
```{r}
#Check normality
hist(Therm_TP4$Chl.prop)
shapiro.test(Therm_TP4$Chl.prop)
#Not normal

hist(log(Therm_TP4$Chl.prop+1))
shapiro.test(log(Therm_TP4$Chl.prop+1))
##Nearly normal

##Model with log +1 transformation
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_Chl.lme_TP4<-lmer(log(Chl.prop+1)~Origin*Site+(1|Genotype), data=Therm_TP4)

##Check residuals
Tol_Chl.lme_res_TP4 <- simulateResiduals(fittedModel = Tol_Chl.lme_TP4, plot = F)
plot(Tol_Chl.lme_res_TP4)

##Model results
summary(Tol_Chl.lme_TP4)

eta_squared(Tol_Chl.lme_TP4)

```


Effect size of Origin for each Site
```{r}
##KL
Tol_Chl.lme_TP4_KL<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="KL"),])
summary(Tol_Chl.lme_TP4_KL)
eta_squared(Tol_Chl.lme_TP4_KL)


##SS
Tol_Chl.lme_TP4_SS<-lmer(log(Chl.prop+1)~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="SS"),])
summary(Tol_Chl.lme_TP4_SS)
eta_squared(Tol_Chl.lme_TP4_SS)

```


```{r Plot Chlorophyll Retention TP4}
##Summary statistics by Site and Origin
TP4_TolChl.sum<-summarySE(Therm_TP4, measurevar="Chl.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP4_TolChl.plot<-ggplot(TP4_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
  ylim(0, 0.35); TP4_TolChl.plot
```


#### FvFm
```{r}
#Check normality
hist(Therm_TP4$PAM.prop)
shapiro.test(Therm_TP4$PAM.prop)
#Not normal

hist(log(Therm_TP4$PAM.prop+1))
shapiro.test(log(Therm_TP4$PAM.prop+1))
##Still not normal

##Model
#Function of Site and Origin, with Genotype as a Random effect
#Interaction between Site and Origin
Tol_PAM.lme_TP4<-lmer(PAM.prop~Origin*Site+(1|Genotype), data=Therm_TP4)

##Check residuals
Tol_PAM.lme_res_TP4 <- simulateResiduals(fittedModel = Tol_PAM.lme_TP4, plot = F)
plot(Tol_PAM.lme_res_TP4)

##Model results
summary(Tol_PAM.lme_TP4)

eta_squared(Tol_PAM.lme_TP4)

```


Effect size of Origin for each Site
```{r}
##KL
Tol_PAM.lme_TP4_KL<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="KL"),])
summary(Tol_PAM.lme_TP4_KL)
eta_squared(Tol_PAM.lme_TP4_KL)


##SS
Tol_PAM.lme_TP4_SS<-lmer(PAM.prop~Origin+(1|Genotype), data=Therm_TP4[which(Therm_TP4$Site=="SS"),])
summary(Tol_PAM.lme_TP4_SS)
eta_squared(Tol_PAM.lme_TP4_SS)

```


```{r Plot FvFm Retention TP4}
##Summary statistics by Site and Origin
TP4_TolPAM.sum<-summarySE(Therm_TP4, measurevar="PAM.prop", groupvars=c("Site", "Origin", "Site.Orig"), na.rm=TRUE)

##Plot Average Retention across Treatments
TP4_TolPAM.plot<-ggplot(TP4_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="Photochemical Efficiency Retention", colour="Origin")+
  ylim(0.6, 1); TP4_TolPAM.plot
```


### Effect Size over Time

```{r}
##Dataframe of effect size results
Tol.ES<-data.frame(TimeP=c(rep("TP1",6), rep("TP2",6), rep("TP3",6), rep("TP4",6)), 
                    Site=c(rep(c("KL", "SS"),12)),
                    Metric=c(rep(c("Sym.prop", "Sym.prop", "Chl.prop", "Chl.prop", "PAM.prop", "PAM.prop"),4)),
                    EtaSq=c(eta_squared(Tol_Sym.lme_TP1_KL)$Eta2_partial,
                             eta_squared(Tol_Sym.lme_TP1_SS)$Eta2_partial,
                             eta_squared(Tol_Chl.lme_TP1_KL)$Eta2_partial,
                             eta_squared(Tol_Chl.lme_TP1_SS)$Eta2_partial,
                             eta_squared(Tol_PAM.lme_TP1_KL)$Eta2_partial,
                             eta_squared(Tol_PAM.lme_TP1_SS)$Eta2_partial, 
                     eta_squared(Tol_Sym.lme_TP2_KL)$Eta2_partial,
                             eta_squared(Tol_Sym.lme_TP2_SS)$Eta2_partial,
                             eta_squared(Tol_Chl.lme_TP2_KL)$Eta2_partial,
                             eta_squared(Tol_Chl.lme_TP2_SS)$Eta2_partial,
                             eta_squared(Tol_PAM.lme_TP2_KL)$Eta2_partial,
                             eta_squared(Tol_PAM.lme_TP2_SS)$Eta2_partial,
                     eta_squared(Tol_Sym.lme_TP3_KL)$Eta2_partial,
                             eta_squared(Tol_Sym.lme_TP3_SS)$Eta2_partial,
                             eta_squared(Tol_Chl.lme_TP3_KL)$Eta2_partial,
                             eta_squared(Tol_Chl.lme_TP3_SS)$Eta2_partial,
                             eta_squared(Tol_PAM.lme_TP3_KL)$Eta2_partial,
                             eta_squared(Tol_PAM.lme_TP3_SS)$Eta2_partial,
                     eta_squared(Tol_Sym.lme_TP4_KL)$Eta2_partial,
                             eta_squared(Tol_Sym.lme_TP4_SS)$Eta2_partial,
                             eta_squared(Tol_Chl.lme_TP4_KL)$Eta2_partial,
                             eta_squared(Tol_Chl.lme_TP4_SS)$Eta2_partial,
                             eta_squared(Tol_PAM.lme_TP4_KL)$Eta2_partial,
                             eta_squared(Tol_PAM.lme_TP4_SS)$Eta2_partial),
                    Pvalue=c(summary(Tol_Sym.lme_TP1_KL)$coefficients[10],
                             summary(Tol_Sym.lme_TP1_SS)$coefficients[10],
                             summary(Tol_Chl.lme_TP1_KL)$coefficients[10],
                             summary(Tol_Chl.lme_TP1_SS)$coefficients[10],
                             summary(Tol_PAM.lme_TP1_KL)$coefficients[10],
                             summary(Tol_PAM.lme_TP1_SS)$coefficients[10], 
                     summary(Tol_Sym.lme_TP2_KL)$coefficients[10],
                             summary(Tol_Sym.lme_TP2_SS)$coefficients[10],
                             summary(Tol_Chl.lme_TP2_KL)$coefficients[10],
                             summary(Tol_Chl.lme_TP2_SS)$coefficients[10],
                             summary(Tol_PAM.lme_TP2_KL)$coefficients[10],
                             summary(Tol_PAM.lme_TP2_SS)$coefficients[10],
                     summary(Tol_Sym.lme_TP3_KL)$coefficients[10],
                             summary(Tol_Sym.lme_TP3_SS)$coefficients[10],
                             summary(Tol_Chl.lme_TP3_KL)$coefficients[10],
                             summary(Tol_Chl.lme_TP3_SS)$coefficients[10],
                             summary(Tol_PAM.lme_TP3_KL)$coefficients[10],
                             summary(Tol_PAM.lme_TP3_SS)$coefficients[10],
                     summary(Tol_Sym.lme_TP4_KL)$coefficients[10],
                             summary(Tol_Sym.lme_TP4_SS)$coefficients[10],
                             summary(Tol_Chl.lme_TP4_KL)$coefficients[10],
                             summary(Tol_Chl.lme_TP4_SS)$coefficients[10],
                             summary(Tol_PAM.lme_TP4_KL)$coefficients[10],
                             summary(Tol_PAM.lme_TP4_SS)$coefficients[10]))


Tol.ES$Sig<-ifelse(Tol.ES$Pvalue<0.001, "***", ifelse(Tol.ES$Pvalue<0.01, "**", ifelse(Tol.ES$Pvalue<0.05, "*", ifelse(Tol.ES$Pvalue<0.1, "-", NA))))
```


#### Symbionts
```{r Plot Effect Size Symbiont Retention}
Tol_Sym.ES.plot<-ggplot(Tol.ES[which(Tol.ES$Metric=="Sym.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Symbiont Retention")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
   labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.4)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Tol_Sym.ES.plot

```


#### Chlorophyll
```{r Plot Effect Size Chlorophyll Retention}
Tol_Chl.ES.plot<-ggplot(Tol.ES[which(Tol.ES$Metric=="Chl.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Chlorophyll Retention")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
   labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.4)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Tol_Chl.ES.plot

```


#### FvFm
```{r Plot Effect Size FvFm Retention}
Tol_PAM.ES.plot<-ggplot(Tol.ES[which(Tol.ES$Metric=="PAM.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
    scale_fill_manual(values=Site.colors.o)+
theme_classic()+
  ggtitle("Photochemical Efficiency Retention")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
   labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.4)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold"); Tol_PAM.ES.plot

```


# Trade Offs

### Average Performance
Calculating the average of Growth (TP1 to TP2) and Thermal Tolerance (Retention of Symbionts, Chlorophyll, and Photochemical Efficiency at TP1) for each Set (Site, Genotype, Origin)

#### Growth
```{r}
##Average
names(Growth)
Growth_Set<-aggregate(Growth$TLE_cm.day, list(Growth$Site, Growth$Genotype, Growth$Orig), mean)
names(Growth_Set)<-c("Site", "Genotype", "Orig", "TLE_cm.day")

##Add Standard Error
Growth_Set$TLE_se<-aggregate(Growth$TLE_cm.day, list(Growth$Site, Growth$Genotype, Growth$Orig), std.error)[,4]
```


#### Tolerance
```{r}
##Average
names(Therm_TP1)
Therm_Set<-aggregate(Therm_TP1[,c(19:21)], list(Therm_TP1$Site, Therm_TP1$Genotype, Therm_TP1$Orig), mean)
names(Therm_Set)<-c("Site", "Genotype", "Orig", "Chl.prop", "Sym.prop", "PAM.prop")

##Add Standard Error
Therm_Set$Chl_se<-aggregate(Therm_TP1$Chl.prop, list(Therm_TP1$Site, Therm_TP1$Genotype, Therm_TP1$Orig), std.error)[,4]

Therm_Set$Sym_se<-aggregate(Therm_TP1$Sym.prop, list(Therm_TP1$Site, Therm_TP1$Genotype, Therm_TP1$Orig), std.error)[,4]

Therm_Set$PAM_se<-aggregate(Therm_TP1$PAM.prop, list(Therm_TP1$Site, Therm_TP1$Genotype, Therm_TP1$Orig), std.error)[,4]
```


#### Merge Growth and Tolerance 
```{r}
Perf_Set<-merge(Growth_Set, Therm_Set, all=TRUE)
Perf_Set$Set<-paste(Perf_Set$Site, Perf_Set$Genotype, Perf_Set$Orig, sep=".")

##Set factor variables
Perf_Set$Site<-factor(Perf_Set$Site, levels=c("KL", "SS"))
Perf_Set$Genotype<-factor(Perf_Set$Genotype, levels=c("AC8", "AC10", "AC12"))
Perf_Set$Orig<-factor(Perf_Set$Orig, levels=c("N", "T"))

##Add a Sample Set Variable
Perf_Set$Set<-paste(Perf_Set$Site, Perf_Set$Genotype, Perf_Set$Orig, sep=".")
Perf_Set$Set<-factor(Perf_Set$Set)

##Add Site.Orig variable
Perf_Set$Site.Orig<-paste(Perf_Set$Site, Perf_Set$Orig, sep=".")
Perf_Set$Site.Orig<-factor(Perf_Set$Site.Orig, levels=c("KL.N", "KL.T", "SS.N", "SS.T"))

```


### Correlation between Growth and Tolerance

#### Symbiont Retention

```{r}
cor.test(Perf_Set$TLE_cm.day, Perf_Set$Sym.prop, method="spearman")
```


```{r Plot Growth vs Symbiont Retention}
TLE_Sym.plot<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=Sym.prop)) + 
   geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
   geom_point(aes(colour=Site.Orig),size=point.sz)+
    scale_colour_manual(values=Orig.colors.o)+
   theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x=expression(paste('Growth Rate (cm day'^-1*")")), 
       y="Symbiont Retention", 
       colour="Site Origin")+
   annotate("text", x = 0.07, y = 0, label=expression(bolditalic(paste(r[S], " = -0.615, p = 0.037"))), size=sig.sz, hjust = 0); TLE_Sym.plot
```


#### Chlorophyll Retention

```{r}
cor.test(Perf_Set$TLE_cm.day, Perf_Set$Chl.prop, method="spearman")
```


```{r Plot Growth vs Chlorophyll Retention}
TLE_Chl.plot<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=Chl.prop)) + 
   geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
   geom_point(aes(colour=Site.Orig),size=point.sz)+
    scale_colour_manual(values=Orig.colors.o)+
   theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x=expression(paste('Growth Rate (cm day'^-1*")")), 
       y="Chlorophyll Retention", 
       colour="Site Origin")+
   annotate("text", x = 0.07, y = 0, label=expression(bolditalic(paste(r[S], " = -0.587, p = 0.049"))), size=sig.sz, hjust = 0); TLE_Chl.plot
```


#### FvFm Retention

```{r}
cor.test(Perf_Set$TLE_cm.day, Perf_Set$PAM.prop, method="spearman")
```


```{r Plot Growth vs FvFm Retention}
TLE_PAM.plot<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=PAM.prop)) + 
   geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
   geom_point(aes(colour=Site.Orig),size=point.sz)+
    scale_colour_manual(values=Orig.colors.o)+
   theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x=expression(paste('Growth Rate (cm day'^-1*")")), 
       y="Photo. Effic. Retention", 
       colour="Site Origin")+
   annotate("text", x = 0.07, y = 0.74, label=expression(bolditalic(paste(r[S], " = -0.678, p = 0.019"))), size=sig.sz, hjust = 0); TLE_PAM.plot
```


# Figures

### Figure 3 Growth and Tolerance ES

#### Adjust Figures for Panel
```{r}
##Growth
TP1.2_Growth.plot.cut<-ggplot(TP1.2_Growth.sum, aes(x=Site, y=TLE_cm.day, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=TLE_cm.day-se, ymax=TLE_cm.day+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), legend.position=c(0.2, 0.8))+
  labs(x="Site and Origin", y=expression(paste('Growth Rate (cm day'^-1*")")), colour="Origin")+
  ylim(0, 0.3)+
    annotate("text", x=2, y=0.24, label="*", size=sig.sz, fontface="bold")+
  geom_bracket(xmin=1, xmax=2, y.position=0.01, label.size=levels.sz, label="**", inherit.aes = FALSE)

##FvFm Retention
Tol_PAM.ES.plot.cut<-ggplot(Tol.ES[which(Tol.ES$Metric=="PAM.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
  scale_fill_manual(values=Site.colors.o)+
  theme_classic()+
  ggtitle("Photochemical Efficiency")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"), legend.position=c(0.8, 0.8))+
  labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.35)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")

##Symbiont Retention
Tol_Sym.ES.plot.cut<-ggplot(Tol.ES[which(Tol.ES$Metric=="Sym.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
  scale_fill_manual(values=Site.colors.o)+
  theme_classic()+
  ggtitle("Symbionts")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
  labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.35)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")


##Chlorophyll Retention
Tol_Chl.ES.plot.cut<-ggplot(Tol.ES[which(Tol.ES$Metric=="Chl.prop"),], aes(x=TimeP, y=EtaSq, fill=Site))+
  geom_bar(stat="identity", position=position_dodge())+
  scale_fill_manual(values=Site.colors.o)+
  theme_classic()+
  ggtitle("Chlorophyll")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, hjust=0.5), axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
  labs(x="Time Point", y=expression(paste("Effect Size (p", eta^2, ")")), colour="Site")+
  ylim(0, 0.35)+
  geom_text(aes(label=Sig), vjust=-0.02, color="black", position=position_dodge(0.9), size=levels.sz, fontface="bold")

```


#### Figure 3
```{r}
##Create Panel
Performance_fig<-plot_grid(TP1.2_Growth.plot.cut, Tol_PAM.ES.plot.cut, 
                      Tol_Sym.ES.plot.cut, Tol_Chl.ES.plot.cut,
                    rel_widths=c(1, 1, 1, 1), 
                    rel_heights = c(1, 1, 1, 1),
                    nrow=2, ncol=2, byrow=T, labels = c("A", "B", "C", "D"))

##Save Figure
ggsave(filename="Figures/03_Performance/Fig3_Performance.png", plot=Performance_fig, dpi=300, width=10, height=8, units="in")

```



### Figure 4 Growth vs Tolerance

#### Adjust Figures for Panel
```{r}
##Growth vs FvFm Retention
TLE_PAM.plot.cut<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=PAM.prop)) + 
   geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
   geom_point(aes(colour=Site.Orig),size=point.sz)+
    scale_colour_manual(values=Orig.colors.o)+
   theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
  labs(x=expression(paste('Growth Rate (cm day'^-1*")")), 
       y="Photo. Effic. Retention", 
       colour="Site Origin")+
   annotate("text", x = 0.07, y = 0.75, label=expression(bolditalic(paste(r[S], " = -0.678, p = 0.019"))), size=sig.sz, hjust = 0)

##Growth vs Symbiont Retention
TLE_Sym.plot.cut<-ggplot(Perf_Set, aes(x=TLE_cm.day, y=Sym.prop)) + 
   geom_smooth(method="lm", color="#AA185AFF", fill="#F8D7BFFF", se=TRUE)+
   geom_point(aes(colour=Site.Orig),size=point.sz)+
    scale_colour_manual(values=Orig.colors.o)+
   theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), legend.position="none")+
  labs(x=expression(paste('Growth Rate (cm day'^-1*")")), 
       y="Symbiont Retention", 
       colour="Site Origin")+
   annotate("text", x = 0.07, y = 0, label=expression(bolditalic(paste(r[S], " = -0.615, p = 0.037"))), size=sig.sz, hjust = 0)
```



#### Figure 4
```{r}
##Create Panel
TradeOff_fig<-plot_grid(TLE_PAM.plot.cut, TLE_Sym.plot.cut, TLE_Chl.plot,
                    rel_widths=c(.75, .75, 1), 
                    rel_heights = c(1, 1, 1),
                    nrow=1, ncol=3, byrow=T, labels = c("A", "B", "C"))

##Save Figure
ggsave(filename="Figures/03_Performance/Fig4_TradeOff.png", plot=TradeOff_fig, dpi=300, width=12, height=4, units="in")

```


### Figure S4 Initial Heat Assay

#### Adjust Figures for Panel
```{r}
##FvFm
IN_PAM.plot.cut<-ggplot(IN_PAM.sum, aes(x=Site.Treat, y=Fv_Fm, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Fv_Fm-se, ymax=Fv_Fm+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
  scale_shape_manual(values=c(1,16))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="Fv/Fm", colour="Site")+
  ylim(0.5, 0.655)

IN_TolPAM.plot.cut<-ggplot(IN_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="Retention", colour="Site")+
  ylim(0.75, 1)+
  annotate("text", x=1.5, y=0.95, label="*", size=sig.sz, fontface="bold")

##Symbionts
IN_Sym.plot.cut<-ggplot(IN_Sym.sum, aes(x=Site.Treat, y=Sym10.6_cm2, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Sym10.6_cm2-se, ymax=Sym10.6_cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
  scale_shape_manual(values=c(1,16))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y=expression(paste('Symbionts ('*10^6,'cells cm'^-2*")")), colour="Site")+
  ylim(0, 1)

IN_TolSym.plot.cut<-ggplot(IN_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="Retention", colour="Site")+
  ylim(.25, .75)+
  annotate("text", x=1.5, y=0.7, label="-", size=sig.sz, fontface="bold")

##Chlorophyll
IN_Chl.plot.cut<-ggplot(IN_Chl.sum, aes(x=Site.Treat, y=Chl_ug.cm2, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Chl_ug.cm2-se, ymax=Chl_ug.cm2+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(aes(shape=Treatment), size=point.sz, position=position_dodge(width=0.5))+
  scale_shape_manual(values=c(1,16))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="Site and Treatment", y=expression(paste('Chlorophyll (\u03BCg cm'^-2*")")), colour="Site")+
  ylim(0, 1.25)

IN_TolChl.plot.cut<-ggplot(IN_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site)) + 
  scale_colour_manual(values=Site.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="Site", y="Retention", colour="Site")+
  ylim(0, 0.25)
```


#### Figure S4
```{r}
##Create Panel
IN_HeatAssay_fig<-plot_grid(IN_PAM.plot.cut, IN_TolPAM.plot.cut, 
                      IN_Sym.plot.cut, IN_TolSym.plot.cut,
                      IN_Chl.plot.cut, IN_TolChl.plot.cut,
                    rel_widths=c(1, 0.75), 
                    rel_heights = c(1, 1, 1),
                    nrow=3, ncol=2, byrow=T, labels = c("A", "B", "C", "D", "E", "F"))

##Save Figure
ggsave(filename="Figures/03_Performance/FigS4_InitialHeatAssay.png", plot=IN_HeatAssay_fig, dpi=300, width=8, height=11, units="in")

```


### Figure S5 Univariate Panels

#### Adjust Figures for Panel
```{r}

##FvFm
TP1_TolPAM.plot.cut<-ggplot(TP1_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  ggtitle("TP1")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="Photo. Effic. Retention", colour="Origin")+
  ylim(0.7, 1)+
  annotate("text", x=1, y=1, label="**", size=sig.sz, fontface="bold")

TP2_TolPAM.plot.cut<-ggplot(TP2_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  ggtitle("TP2")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(0.7, 1)+
  annotate("text", x=1, y=1, label="*", size=sig.sz, fontface="bold")

TP3_TolPAM.plot.cut<-ggplot(TP3_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  ggtitle("TP3")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(0.7, 1)

TP4_TolPAM.plot.cut<-ggplot(TP4_TolPAM.sum, aes(x=Site, y=PAM.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=PAM.prop-se, ymax=PAM.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  ggtitle("TP4")+
  theme(plot.title = element_text(colour="black", size=panel.lab.sz, face="bold", hjust=0.5),axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="", y="", colour="Origin")+
  ylim(0.7, 1)


##Symbionts
TP1_TolSym.plot.cut<-ggplot(TP1_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="Symbiont Retention", colour="Origin")+
  ylim(0.2, 1)+
  annotate("text", x=1, y=0.75, label="*", size=sig.sz, fontface="bold")

TP2_TolSym.plot.cut<-ggplot(TP2_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(0.2, 1)

TP3_TolSym.plot.cut<-ggplot(TP3_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="", y="", colour="Origin")+
  ylim(0.2, 1)

TP4_TolSym.plot.cut<-ggplot(TP4_TolSym.sum, aes(x=Site, y=Sym.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Sym.prop-se, ymax=Sym.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="", y="", colour="Origin")+
  ylim(0.2, 1)


##Chlorophyll
TP1_TolChl.plot.cut<-ggplot(TP1_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="Site and Origin", y="Chlorophyll Retention", colour="Origin")+
  ylim(0, .8)

TP2_TolChl.plot.cut<-ggplot(TP2_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="Site and Origin", y="", colour="Origin")+
  ylim(0, .8)

TP3_TolChl.plot.cut<-ggplot(TP3_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.position="none")+
  labs(x="Site and Origin", y="", colour="Origin")+
  ylim(0, .8)

TP4_TolChl.plot.cut<-ggplot(TP4_TolChl.sum, aes(x=Site, y=Chl.prop, colour=Site.Orig)) + 
  scale_colour_manual(values=Orig.colors.o)+
  geom_errorbar(aes(ymin=Chl.prop-se, ymax=Chl.prop+se), width=cap.sz, linewidth=bar.sz, position=position_dodge(width=0.5)) +
  geom_point(size=point.sz, position=position_dodge(width=0.5))+
  theme_classic()+
  theme(axis.title.x = element_text(size = axis.title.sz), axis.title.y = element_text(size = axis.title.sz), 
        axis.text.x=element_text(size=axis.txt.sz, colour="black"), axis.text.y=element_text(size=axis.txt.sz, colour="black"), 
        legend.text=element_text(size=leg.txt.sz), legend.title=element_text(size=leg.title.sz), 
        legend.box.background = element_rect(color = "black"))+
  labs(x="Site and Origin", y="", colour="Origin")+
  ylim(0, .8)

```


#### Figure S5
```{r}
##Create Panel
Tol_fig<-plot_grid(
  TP1_TolPAM.plot.cut,TP2_TolPAM.plot.cut,TP3_TolPAM.plot.cut,TP4_TolPAM.plot.cut,
  TP1_TolSym.plot.cut,TP2_TolSym.plot.cut,TP3_TolSym.plot.cut,TP4_TolSym.plot.cut,
  TP1_TolChl.plot.cut,TP2_TolChl.plot.cut,TP3_TolChl.plot.cut,TP4_TolChl.plot.cut,
                    rel_widths=c(rep(c(0.7, 0.7, 0.7,1),3)), 
                    rel_heights = c(1,0.85,0.85),
                    nrow=3, ncol=4, byrow=T, labels = NULL)

##Save Figure
ggsave(filename="Figures/03_Performance/FigS5_ToleranceMetrics.png", plot=Tol_fig, dpi=300, width=12, height=10, units="in")

```


# Tables

### Table S5

```{r}
##Dataframe of Performance Results 
##Model Results
TableS5<-data.frame(rbind(
   summary(TLE.lme)$coefficients[-1,],summary(TLE.lme_KL)$coefficients[-1,],
   summary(TLE.lme_SS)$coefficients[-1,],
   
   summary(Tol_PAM.lme_TP1)$coefficients[-1,],summary(Tol_PAM.lme_TP1_KL)$coefficients[-1,],
   summary(Tol_PAM.lme_TP1_SS)$coefficients[-1,],
   summary(Tol_Sym.lme_TP1)$coefficients[-1,],summary(Tol_Sym.lme_TP1_KL)$coefficients[-1,],
   summary(Tol_Sym.lme_TP1_SS)$coefficients[-1,], 
   summary(Tol_Chl.lme_TP1)$coefficients[-1,],summary(Tol_Chl.lme_TP1_KL)$coefficients[-1,],
   summary(Tol_Chl.lme_TP1_SS)$coefficients[-1,], 
   
    summary(Tol_PAM.lme_TP2)$coefficients[-1,],summary(Tol_PAM.lme_TP2_KL)$coefficients[-1,],
   summary(Tol_PAM.lme_TP2_SS)$coefficients[-1,],
   summary(Tol_Sym.lme_TP2)$coefficients[-1,],summary(Tol_Sym.lme_TP2_KL)$coefficients[-1,],
   summary(Tol_Sym.lme_TP2_SS)$coefficients[-1,], 
   summary(Tol_Chl.lme_TP2)$coefficients[-1,],summary(Tol_Chl.lme_TP2_KL)$coefficients[-1,],
   summary(Tol_Chl.lme_TP2_SS)$coefficients[-1,], 
   
      summary(Tol_PAM.lme_TP3)$coefficients[-1,],summary(Tol_PAM.lme_TP3_KL)$coefficients[-1,],
   summary(Tol_PAM.lme_TP3_SS)$coefficients[-1,],
   summary(Tol_Sym.lme_TP3)$coefficients[-1,],summary(Tol_Sym.lme_TP3_KL)$coefficients[-1,],
   summary(Tol_Sym.lme_TP3_SS)$coefficients[-1,], 
   summary(Tol_Chl.lme_TP3)$coefficients[-1,],summary(Tol_Chl.lme_TP3_KL)$coefficients[-1,],
   summary(Tol_Chl.lme_TP3_SS)$coefficients[-1,], 
   
      summary(Tol_PAM.lme_TP4)$coefficients[-1,],summary(Tol_PAM.lme_TP4_KL)$coefficients[-1,],
   summary(Tol_PAM.lme_TP4_SS)$coefficients[-1,],
   summary(Tol_Sym.lme_TP4)$coefficients[-1,],summary(Tol_Sym.lme_TP4_KL)$coefficients[-1,],
   summary(Tol_Sym.lme_TP4_SS)$coefficients[-1,], 
   summary(Tol_Chl.lme_TP4)$coefficients[-1,],summary(Tol_Chl.lme_TP4_KL)$coefficients[-1,],
   summary(Tol_Chl.lme_TP4_SS)$coefficients[-1,]))

names(TableS5)<-c("Estimate", "SE", "DF", "t", "p") 

##Metadata 
TableS5$Type<-c(rep("Growth", 5), rep("Tolerance", 60))
TableS5$Metric<-c(rep("Growth", 5), rep(c(rep("FvFm", 5), rep("Symbionts", 5), rep("Chlorophyll", 5)), 4))
TableS5$TimeP<-c(rep("TP1.2", 5), rep("TP1",15), rep("TP2",15), rep("TP3",15), rep("TP4",15))
TableS5$Predictor<-c(rep(c("Origin", "Site", "Origin x Site", "KL Origin", "SS Origin"), 13))

##Effect Size  
TableS5$EtaSq<-c(
  eta_squared(TLE.lme)$Eta2_partial,eta_squared(TLE.lme_KL)$Eta2_partial,
   eta_squared(TLE.lme_SS)$Eta2_partial,
   
   eta_squared(Tol_PAM.lme_TP1)$Eta2_partial,eta_squared(Tol_PAM.lme_TP1_KL)$Eta2_partial,
   eta_squared(Tol_PAM.lme_TP1_SS)$Eta2_partial,
   eta_squared(Tol_Sym.lme_TP1)$Eta2_partial,eta_squared(Tol_Sym.lme_TP1_KL)$Eta2_partial,
   eta_squared(Tol_Sym.lme_TP1_SS)$Eta2_partial, 
   eta_squared(Tol_Chl.lme_TP1)$Eta2_partial,eta_squared(Tol_Chl.lme_TP1_KL)$Eta2_partial,
   eta_squared(Tol_Chl.lme_TP1_SS)$Eta2_partial, 
   
    eta_squared(Tol_PAM.lme_TP2)$Eta2_partial,eta_squared(Tol_PAM.lme_TP2_KL)$Eta2_partial,
   eta_squared(Tol_PAM.lme_TP2_SS)$Eta2_partial,
   eta_squared(Tol_Sym.lme_TP2)$Eta2_partial,eta_squared(Tol_Sym.lme_TP2_KL)$Eta2_partial,
   eta_squared(Tol_Sym.lme_TP2_SS)$Eta2_partial, 
   eta_squared(Tol_Chl.lme_TP2)$Eta2_partial,eta_squared(Tol_Chl.lme_TP2_KL)$Eta2_partial,
   eta_squared(Tol_Chl.lme_TP2_SS)$Eta2_partial, 
   
      eta_squared(Tol_PAM.lme_TP3)$Eta2_partial,eta_squared(Tol_PAM.lme_TP3_KL)$Eta2_partial,
   eta_squared(Tol_PAM.lme_TP3_SS)$Eta2_partial,
   eta_squared(Tol_Sym.lme_TP3)$Eta2_partial,eta_squared(Tol_Sym.lme_TP3_KL)$Eta2_partial,
   eta_squared(Tol_Sym.lme_TP3_SS)$Eta2_partial, 
   eta_squared(Tol_Chl.lme_TP3)$Eta2_partial,eta_squared(Tol_Chl.lme_TP3_KL)$Eta2_partial,
   eta_squared(Tol_Chl.lme_TP3_SS)$Eta2_partial, 
   
      eta_squared(Tol_PAM.lme_TP4)$Eta2_partial,eta_squared(Tol_PAM.lme_TP4_KL)$Eta2_partial,
   eta_squared(Tol_PAM.lme_TP4_SS)$Eta2_partial,
   eta_squared(Tol_Sym.lme_TP4)$Eta2_partial,eta_squared(Tol_Sym.lme_TP4_KL)$Eta2_partial,
   eta_squared(Tol_Sym.lme_TP4_SS)$Eta2_partial, 
   eta_squared(Tol_Chl.lme_TP4)$Eta2_partial,eta_squared(Tol_Chl.lme_TP4_KL)$Eta2_partial,
   eta_squared(Tol_Chl.lme_TP4_SS)$Eta2_partial)

TableS5<-TableS5[,c(6:9, 1:5, 10)]

rownames(TableS5)<-NULL

##Save Table
write.csv(TableS5, "Outputs/TableS5.csv", row.names=FALSE)

```


### Table S6
```{r}
##Dataframe of Initial Thermal Tolerance Assay Results 
##Model Results
TableS6.model<-data.frame(rbind(
  summary(PAM.lme_IN)$coefficients[-1,], summary(Sym.lme_IN)$coefficients[-1,], summary(Chl.lme_IN)$coefficients[-1,]))

names(TableS6.model)<-c("Estimate", "SE", "DF", "t", "p") 

TableS6.emmeans<-data.frame(rbind(
data.frame(rbind(emmeans(PAM.lme_IN, pairwise ~ Site | Treatment)$contrasts[1], emmeans(PAM.lme_IN, pairwise ~ Site | Treatment)$contrasts[2], emmeans(PAM.lme_IN, pairwise ~ Treatment | Site)$contrasts[1], emmeans(PAM.lme_IN, pairwise ~ Treatment | Site)$contrasts[2]))[,-c(1:3)], 
data.frame(rbind(emmeans(Sym.lme_IN, pairwise ~ Site | Treatment)$contrasts[1], emmeans(Sym.lme_IN, pairwise ~ Site | Treatment)$contrasts[2], emmeans(Sym.lme_IN, pairwise ~ Treatment | Site)$contrasts[1], emmeans(Sym.lme_IN, pairwise ~ Treatment | Site)$contrasts[2]))[,-c(1:3)],
data.frame(rbind(emmeans(Chl.lme_IN, pairwise ~ Site | Treatment)$contrasts[1], emmeans(Chl.lme_IN, pairwise ~ Site | Treatment)$contrasts[2], emmeans(Chl.lme_IN, pairwise ~ Treatment | Site)$contrasts[1], emmeans(Chl.lme_IN, pairwise ~ Treatment | Site)$contrasts[2]))[,-c(1:3)]))

names(TableS6.emmeans)<-c("Estimate", "SE", "DF", "t", "p") 

TableS6<-rbind(TableS6.model[1:3,], TableS6.emmeans[1:4,], TableS6.model[4:6,], TableS6.emmeans[5:8,], TableS6.model[7:9,], TableS6.emmeans[9:12,])

##Metadata 
TableS6$Metric<-c(rep("FvFm", 7), rep("Symbionts", 7), rep("Chlorophyll", 7))
TableS6$Predictor<-c(rep(c("Site", "Treatment", "Site x Treatment", "Control Site", "Heated Site", "KL Treatment", "SS Treatment"), 3))


##Effect Size  
TableS6$EtaSq<-c(eta_squared(PAM.lme_IN)$Eta2_partial, NA, NA, NA, NA, eta_squared(Sym.lme_IN)$Eta2_partial, NA, NA, NA, NA, eta_squared(Chl.lme_IN)$Eta2_partial, NA, NA, NA, NA)

TableS6<-TableS6[,c(6:7, 1:5, 8)]

rownames(TableS6)<-NULL

##Save Table
write.csv(TableS6, "Outputs/TableS6.csv", row.names=FALSE)
  
```

